Denoising Task Routing for Diffusion Models

Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion models. In this paper, we present Denoising Task Routing (DTR), a simple add-on strategy for existing diffusion model architectures to establish distinct information pathways for individual tasks within a single architecture by selectively activating subsets of channels in the model. What makes DTR particularly compelling is its seamless integration of prior knowledge of denoising tasks into the framework: (1) Task Affinity: DTR activates similar channels for tasks at adjacent timesteps and shifts activated channels as sliding windows through timesteps, capitalizing on the inherent strong affinity between tasks at adjacent timesteps. (2) Task Weights: During the early stages (higher timesteps) of the denoising process, DTR assigns a greater number of task-specific channels, leveraging the insight that diffusion models prioritize reconstructing global structure and perceptually rich contents in earlier stages, and focus on simple noise removal in later stages. Our experiments reveal that DTR not only consistently boosts diffusion models' performance across different evaluation protocols without adding extra parameters but also accelerates training convergence. Finally, we show the complementarity between our architectural approach and existing MTL optimization techniques, providing a more complete view of MTL in the context of diffusion training. Significantly, by leveraging this complementarity, we attain matched performance of DiT-XL using the smaller DiT-L with a reduction in training iterations from 7M to 2M.Comment: ICLR 202

Contributors to Reduced Life Expectancy Among Native Americans in the Four Corners States

To assess trends in life expectancy and the contribution of specific causes of death to Native American-White longevity gaps in the Four Corners states, we used death records from the National Center for Health Statistics and population estimates from the U.S. Census Bureau from 1999–2017 to generate period life tables and decompose racial gaps in life expectancy. Native American-White life expectancy gaps narrowed between 2001 and 2012 but widened thereafter, reaching 4.92 years among males and 2.06 years among females in 2015. The life expectancy disadvantage among Native American males was primarily attributable to motor vehicle accidents (0.96 years), liver disease (1.22 years), and diabetes (0.78 years). These causes of deaths were also primary contributors to the gap among females, forming three successive waves of mortality that occurred in young adulthood, midlife, and late adulthood, respectively, among Native American males and females. Interventions to reduce motor vehicle accidents in early adulthood, alcohol-related mortality in midlife, and diabetes complications at older ages could reduce Native American-White longevity disparities in the Four Corners states

Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling

There are two de facto standard architectures in recent computer vision: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Strong inductive biases of convolutions help the model learn sample effectively, but such strong biases also limit the upper bound of CNNs when sufficient data are available. On the contrary, ViT is inferior to CNNs for small data but superior for sufficient data. Recent approaches attempt to combine the strengths of these two architectures. However, we show these approaches overlook that the optimal inductive bias also changes according to the target data scale changes by comparing various models' accuracy on subsets of sampled ImageNet at different ratios. In addition, through Fourier analysis of feature maps, the model's response patterns according to signal frequency changes, we observe which inductive bias is advantageous for each data scale. The more convolution-like inductive bias is included in the model, the smaller the data scale is required where the ViT-like model outperforms the ResNet performance. To obtain a model with flexible inductive bias on the data scale, we show reparameterization can interpolate inductive bias between convolution and self-attention. By adjusting the number of epochs the model stays in the convolution, we show that reparameterization from convolution to self-attention interpolates the Fourier analysis pattern between CNNs and ViTs. Adapting these findings, we propose Progressive Reparameterization Scheduling (PRS), in which reparameterization adjusts the required amount of convolution-like or self-attention-like inductive bias per layer. For small-scale datasets, our PRS performs reparameterization from convolution to self-attention linearly faster at the late stage layer. PRS outperformed previous studies on the small-scale dataset, e.g., CIFAR-100.Comment: Accepted at VIPriors ECCVW 2022, camera-ready versio

A Robot Operating System Framework for Secure UAV Communications

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on sev-eral perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Gestational Age at Birth and Risk of Developmental Delay: The Upstate KIDS Study

Objective—To model the association between gestational age at birth and early child development through 3 years of age. Study Design—Development of 5868 children in Upstate KIDS (New York State; 2008–2014) was assessed at 7 time-points using the Ages and Stages Questionnaire (ASQ). The ASQ was implemented using gestational age corrected dates of birth at 4, 8, 12, 18, 24, 30, and 36 months. Whether children were eligible for developmental services from the Early Intervention Program (EIP) was determined through linkage. Gestational age was based on vital records. Statistical models adjusted for covariates including sociodemographic factors, maternal smoking and plurality. Results——Compared to gestational age of 39 weeks, adjusted odds ratios (aOR) and 95% confidence intervals of failing the ASQ for children delivered at \u3c 32, 32–34, 35–36, 37, 38, and 40 weeks gestational age were: 5.32 (3.42, 8.28), 2.43 (1.60, 3.69), 1.38 (1.00, 1.90), 1.37 (0.98, 1.90), 1.29 (0.99, 1.67), 0.73 (0.55, 0.96), and 0.51 (0.32, 0.82). Similar risks of being eligible for EIP services were observed (aOR: 4.19, 2.10, 1.29, 1.20, 1.01, 1.00 (ref), 0.92, 0.78, respectively for \u3c 32, 32–34, 37, 38, 39 (ref), 40, 41 weeks). Conclusion—Gestational age was inversely associated with developmental delays for all gestational ages. Evidence from our study is potentially informative for low-risk deliveries at 39 weeks but it is notable that deliveries at 40 weeks exhibited further lower risk

Posterior Epidural Migration of a Lumbar Intervertebral Disc Fragment Resembling a Spinal Tumor: A Case Report

Posterior epidural migration of a lumbar intervertebral disc fragment (PEMLIF) is uncommon because of anatomical barriers. It is difficult to diagnose PEMLIF definitively because of its relatively rare incidence and the ambiguity of radiological findings resembling spinal tumors. This case report describes a 76-year-old man with sudden-onset weakness and pain in both legs. Electromyography revealed bilateral lumbosacral polyradiculopathy with a mass-like lesion in L2-3 dorsal epidural space on lumbosacral magnetic resonance imaging (MRI). The lesion showed peripheral rim enhancement on T1-weighted MRI with gadolinium administration. The patient underwent decompressive L2-3 central laminectomy, to remove the mass-like lesion. The excised lesion was confirmed as an intervertebral disc. The possibility of PEMLIF should be considered when rim enhancement is observed in the epidural space on MRI scans and electrodiagnostic features of polyradiculopathy with sudden symptoms of cauda equina syndrome

Association of pathway mutation with survival after recurrence in colorectal cancer patients treated with adjuvant fluoropyrimidine and oxaliplatin chemotherapy

Background Although the prognostic biomarkers associated with colorectal cancer (CRC) survival are well known, there are limited data on the association between the molecular characteristics and survival after recurrence (SAR). The purpose of this study was to assess the association between pathway mutations and SAR. Methods Of the 516 patients with stage III or high risk stage II CRC patients treated with surgery and adjuvant chemotherapy, 87 who had distant recurrence were included in the present study. We analyzed the association between SAR and mutations of 40 genes included in the five critical pathways of CRC (WNT, P53, RTK-RAS, TGF-β, and PI3K). Results Mutation of genes within the WNT, P53, RTK-RAS, TGF-β, and PI3K pathways were shown in 69(79.3%), 60(69.0%), 57(65.5%), 21(24.1%), and 19(21.8%) patients, respectively. Patients with TGF-β pathway mutation were younger and had higher incidence of mucinous adenocarcinoma (MAC) histology and microsatellite instability-high. TGF-β pathway mutation (median SAR of 21.6 vs. 44.4 months, p = 0.021) and MAC (20.0 vs. 44.4 months, p = 0.003) were associated with poor SAR, and receiving curative resection after recurrence was associated with favorable SAR (Not reached vs. 23.6 months, p <  0.001). Mutations in genes within other critical pathways were not associated with SAR. When MAC was excluded as a covariate, multivariate analysis revealed TGF-β pathway mutation and curative resection after distant recurrence as an independent prognostic factor for SAR. The impact of TGF-β pathway mutations were predicted using the PROVEAN, SIFT, and PolyPhen-2. Among 25 mutations, 23(92.0%)-24(96.0%) mutations were predicted to be damaging mutation. Conclusions Mutation in genes within TGF-β pathway may have negative prognostic role for SAR in CRC. Other pathway mutations were not associated with SAR.This research was supported by the Seoul National University Hospital (SNUH) Research Fund (03–2014-0440) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI14C1277 and HI13C2163). The funding bodies had no influence on the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

Bridging Implicit and Explicit Geometric Transformation for Single-Image View Synthesis

Creating novel views from a single image has achieved tremendous strides with advanced autoregressive models, as unseen regions have to be inferred from the visible scene contents. Although recent methods generate high-quality novel views, synthesizing with only one explicit or implicit 3D geometry has a trade-off between two objectives that we call the "seesaw" problem: 1) preserving reprojected contents and 2) completing realistic out-of-view regions. Also, autoregressive models require a considerable computational cost. In this paper, we propose a single-image view synthesis framework for mitigating the seesaw problem while utilizing an efficient non-autoregressive model. Motivated by the characteristics that explicit methods well preserve reprojected pixels and implicit methods complete realistic out-of-view regions, we introduce a loss function to complement two renderers. Our loss function promotes that explicit features improve the reprojected area of implicit features and implicit features improve the out-of-view area of explicit features. With the proposed architecture and loss function, we can alleviate the seesaw problem, outperforming autoregressive-based state-of-the-art methods and generating an image $\approx$100 times faster. We validate the efficiency and effectiveness of our method with experiments on RealEstate10K and ACID datasets.Comment: TPAMI 202

An Empirical Study on Cyber Attack and Defense of Robot Operating System using Security Platform in UAV

Recently, UAVs (Unmanned Aerial Vehicles), which represent a key application of cyber -physical systems, have been used for malicious intent. Accordingly, research on the neutralization of UAV has become critical. The UAV neutralization process can be divided into three phases. The identification stage is aimed at distinguishing whether the UAVs are friendly or adversarial. Next, the actual mission of the UAVs is neutralized. Subsequently, post-processing is performed to guide the UAV to safe areas. Because the existing UAV neutralization studies did not consider phase 3, secondary damage such as that to people and property may occur after an attack. Therefore, we propose a UAV neutralization method in which phase 3 applied. In this paper, to implement functions such as autonomous driving, navigation, and collision avoidance, we investigate the vulnerability of the MAVROS environment commonly used in unmanned vehicles and clarify the method of attack that exploits the vulnerability. Additionally, we propose a MAVROS API to enhance the security to prevent these vulnerabilities. Finally, we verify the proposed attacks and security through empirical research by considering UAVs with MAVROS. © ICROS 2021.1

Clinical course and outcome in patients with severe dysphagia after lateral medullary syndrome

Background The objective of this study was to investigate the clinical course and final outcome in patients afflicted with severe dysphagia following a diagnosis of lateral medullary syndrome (LMS). Methods The patients with severe dysphagia after LMS admitted to a rehabilitation unit were included and their respective clinical data were prospectively collected. The criteria of ‘severe dysphagia’ was defined as the condition that showed decreased pharyngeal constriction with no esophageal passage in a videofluoroscopic swallowing study (VFSS) and initially required enteral tube feeding. The data included VFSS findings, types of diet and postural modification, penetration-aspiration scale (PAS) and functional oral intake scale (FOIS). Results A total of 11 patients were included and VFSS was performed every 2 weeks after stroke onset. Esophageal passage began to show at an average 34.7 ± 18.3 days, and the patients were able to begin consuming a partial oral diet with postural modification. It was 52.2 ± 21.8 days till they were advanced to a full oral diet. PAS and FOIS were significantly improved over time. Conclusions Patients with severe dysphagia after LMS were able to tolerate a partial oral diet at about 5 weeks following onset, and they were advanced to a normal diet after 10 weeks. This clinical course might help in predicting the prognosis, as well as assist in making practical decisions regarding a rehabilitation program