Multi-Architecture Multi-Expert Diffusion Models

Diffusion models have achieved impressive results in generating diverse and realistic data by employing multi-step denoising processes. However, the need for accommodating significant variations in input noise at each time-step has led to diffusion models requiring a large number of parameters for their denoisers. We have observed that diffusion models effectively act as filters for different frequency ranges at each time-step noise. While some previous works have introduced multi-expert strategies, assigning denoisers to different noise intervals, they overlook the importance of specialized operations for high and low frequencies. For instance, self-attention operations are effective at handling low-frequency components (low-pass filters), while convolutions excel at capturing high-frequency features (high-pass filters). In other words, existing diffusion models employ denoisers with the same architecture, without considering the optimal operations for each time-step noise. To address this limitation, we propose a novel approach called Multi-architecturE Multi-Expert (MEME), which consists of multiple experts with specialized architectures tailored to the operations required at each time-step interval. Through extensive experiments, we demonstrate that MEME outperforms large competitors in terms of both generation performance and computational efficiency

Addressing Negative Transfer in Diffusion Models

Diffusion-based generative models have achieved remarkable success in various domains. It trains a model on denoising tasks that encompass different noise levels simultaneously, representing a form of multi-task learning (MTL). However, analyzing and improving diffusion models from an MTL perspective remains under-explored. In particular, MTL can sometimes lead to the well-known phenomenon of $\textit{negative transfer}$, which results in the performance degradation of certain tasks due to conflicts between tasks. In this paper, we aim to analyze diffusion training from an MTL standpoint, presenting two key observations: $\textbf{(O1)}$ the task affinity between denoising tasks diminishes as the gap between noise levels widens, and $\textbf{(O2)}$ negative transfer can arise even in the context of diffusion training. Building upon these observations, our objective is to enhance diffusion training by mitigating negative transfer. To achieve this, we propose leveraging existing MTL methods, but the presence of a huge number of denoising tasks makes this computationally expensive to calculate the necessary per-task loss or gradient. To address this challenge, we propose clustering the denoising tasks into small task clusters and applying MTL methods to them. Specifically, based on $\textbf{(O2)}$, we employ interval clustering to enforce temporal proximity among denoising tasks within clusters. We show that interval clustering can be solved with dynamic programming and utilize signal-to-noise ratio, timestep, and task affinity for clustering objectives. Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods. We validate the proposed clustering and its integration with MTL methods through various experiments, demonstrating improved sample quality of diffusion models.Comment: 22 pages, 12 figures, under revie

Scar folding for the treatment of nostril stenosis after open rhinoplasty: a case report

A 25-year-old woman was referred for discomfort when breathing through her left nose. The patient had undergone augmentation rhinoplasty 5 years ago, after which hypertrophic scarring occurred in the left nostril. Several corticosteroid injections were administered as the first line of treatment, but with no symptom improvement. Therefore, we proceeded with surgical scar removal, with the use of a nasal conformer. However, scarring in the left nostril recurred. Accordingly, we proceeded with further surgical treatment using the scar folding technique. After scar folding, neither scarring nor nostril stenosis recurred during 1 year of postoperative follow-up. To summarize, herein, we report a case of hypertrophic scarring in the nostril that was successfully treated with the scar folding technique

Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies

Background: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. Methods: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. Results: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. Conclusions: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®

Elastic Binder for High-Performance Sulfide-Based All-Solid-State Batteries

Sulfide-based all-solid-state batteries (ASSBs) offer en-hanced safety and potentially high energy density. Particularly, an"anode-less"electrode containing metallic seeds that form a solid-solution with lithium was recently introduced to improve the cycle life ofsulfide-based ASSB cells. However, this anode-less electrode is graduallydestabilized because the metal particles undergo severe volumeexpansion during repeated cycling. Furthermore, the irreversibility ofthe electrode in early cycles impairs the energy density of the cellsignificantly. Herein, we introduce an elastic polymer known as"Spandex"as a binder for the silver-carbon composite. The soft andhard segments of this binder act synergistically in that the former engagesin strong hydrogen bonding with the active material and the latterpromotes elastic adjustment of the binder network. This binder designsignificantly improves the charge-discharge reversibility and long-termcyclability of the anode-less ASSB cell and provides insights into elastic binder systems for high-capacity ASSB anodes thatundergo a large volume expansion.N

Room-Temperature Anode-Less All-Solid-State Batteries via the Conversion Reaction of Metal Fluorides

All-solid-state batteries (ASSBs) that employ anode-less electrodes have drawn attention from across the battery community because they offer competitive energy densities and a markedly improved cycle life. Nevertheless, the composite matrices of anode-less electrodes impose a substantial barrier for lithium-ion diffusion and inhibit operation at room temperature. To overcome this drawback, here, the conversion reaction of metal fluorides is exploited because metallic nanodomains formed during this reaction induce an alloying reaction with lithium ions for uniform and sustainable lithium (de)plating. Lithium fluoride (LiF), another product of the conversion reaction, prevents the agglomeration of the metallic nanodomains and also protects the electrode from fatal lithium dendrite growth. A systematic analysis identifies silver (I) fluoride (AgF) as the most suitable metal fluoride because the silver nanodomains can accommodate the solid-solution mechanism with a low nucleation overpotential. AgF-based full cells attain reliable cycling at 25 degrees C even with an exceptionally high areal capacity of 9.7 mAh cm(-2) (areal loading of LiNi0.8Co0.1Mn0.1O2 = 50 mg cm(-2)). These results offer useful insights into designing materials for anode-less electrodes for sulfide-based ASSBs.N