Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called masked-DDPM (mDPPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in DPPM models for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Code is available at https://github.com/hasan1292/mDDPM.Comment: Accepted in MICCAI 2023 Workshop

The peroxisome proliferators-ativated receptor gamma (PPARG) gene polymorphisms and associations with body measurements of cattle

Peroxisome proliferators-activated receptor gamma (PPARG) is an important regulator in the regulation of adipose differentiation and development. The mutations of the PPARG in human had been shown to be associated with type II diabetes, fat distribution and body weight. The functional importance of the PPARG makes it a good candidate to search molecular markers in marker assistant selection in cattle breeding. All the mRNA region of the PPARG gene within 760 individuals of four Chinese cattle breeds was scanned and four single nucleotide polymorphisms (SNPs) (-110G>C, -27C>T, +20A>G, +1344G>T) of the PPARG were detected in three Chinese indigenous cattle breeds (Qinchuan, Nangyang and Jiaxian cattle), rather than Chinese Holstein cattle. The mutations -110G>C, -27C>T and +20A>G located in the Exon1 of the PPARG and were under linkage disequilibrium. The individuals with these three mutations had smaller body measurements. This information could help animal scientists to develop genetic markers or biomarkers to assist with beef production.Keywords: Peroxisome proliferators-activated receptor gamma (PPARG) gene, polymorphisms, cattle, association analysi

CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction

Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments. However, data privacy is a crucial concern in the interaction between humans and robots, as companies need to protect sensitive data while machine learning algorithms require access to large datasets. Federated Learning (FL) offers a solution by enabling the distributed training of models without sharing raw data. Despite extensive research on Federated learning (FL) for tasks such as natural language processing (NLP) and image classification, the question of how to use FL for HRI remains an open research problem. The traditional FL approach involves transmitting large neural network parameter matrices between the server and clients, which can lead to high communication costs and often becomes a bottleneck in FL. This paper proposes a communication-efficient FL framework for human-robot interaction (CEFHRI) to address the challenges of data heterogeneity and communication costs. The framework leverages pre-trained models and introduces a trainable spatiotemporal adapter for video understanding tasks in HRI. Experimental results on three human-robot interaction benchmark datasets: HRI30, InHARD, and COIN demonstrate the superiority of CEFHRI over full fine-tuning in terms of communication costs. The proposed methodology provides a secure and efficient approach to HRI federated learning, particularly in industrial environments with data privacy concerns and limited communication bandwidth. Our code is available at https://github.com/umarkhalidAI/CEFHRI-Efficient-Federated-Learning.Comment: Accepted in IROS 202

Detection and localization of closely distributed damages via lamb wave sparse reconstruction

Ultrasonic Lamb wave is a promising tool for structural health monitoring and nondestructive evaluation of plate-like structures. Using an array with several piezoelectric discs for damage imaging (i.e. visual detection and localization) is of interest. Commonly used delay-and-sum method is limited for overlapped signals when several damages are closely distributed in the structure. To overcome this limitation, modal-based sparse reconstruction imaging method is applied for adjacent damages in this study. Firstly, Lamb wave dispersion curve is obtained by solving the Rayleigh-Lamb equations. Subsequently, propagation modal of the damage-reflected signal is constructed based on the solved dispersion curve. Finally, the modal is used for damage imaging via sparse reconstruction and basis pursuit de-noising. Experimental data measured in an aluminum plate is considered, and the result demonstrates that the sparse reconstruction imaging method is effective to detect and localize closely distributed damages in the presence of signal overlapping

Aqua­(hippurato)bis­(1,10-phenanthroline)cobalt(II) nitrate monohydrate

In the title compound, [Co(C9H8NO3)(C12H8N2)2(H2O)]NO3·H2O, the CoII atom is six-coordinated by a carboxylate O atom of the hippurate (Hc) anion, a water O atom and four N atoms from two 1,10-phenanthroline ligands in a distorted octa­hedral geometry. The uncoordinated O atom of the hippuric acid anion is involved in an intra­molecular hydrogen bond to the coordinated water mol­ecule. The crystal packing is stabilized by inter­molecular O—H⋯O hydrogen bonds involving the Hc anions, the coordinated water mol­ecule, the nitrate anion and the uncoordinated water mol­ecule

Improved lower bounds on genuine-multipartite-entanglement concurrence

Genuine-multipartite-entanglement (GME) concurrence is a measure of genuine multipartite entanglement that generalizes the well-known notion of concurrence. We define an observable for GME concurrence. The observable permits us to avoid full state tomography and leads to different analytic lower bounds. By means of explicit examples we show that entanglement criteria based on the bounds have a better performance with respect to the known methods.Comment: 17 pages, 1 EPS figure; v3 is in one column to improve readability of equation