Diffusion Conditional Expectation Model for Efficient and Robust Target Speech Extraction

Target Speech Extraction (TSE) is a crucial task in speech processing that focuses on isolating the clean speech of a specific speaker from complex mixtures. While discriminative methods are commonly used for TSE, they can introduce distortion in terms of speech perception quality. On the other hand, generative approaches, particularly diffusion-based methods, can enhance speech quality perceptually but suffer from slower inference speed. We propose an efficient generative approach named Diffusion Conditional Expectation Model (DCEM) for TSE. It can handle multi- and single-speaker scenarios in both noisy and clean conditions. Additionally, we introduce Regenerate-DCEM (R-DCEM) that can regenerate and optimize speech quality based on pre-processed speech from a discriminative model. Our method outperforms conventional methods in terms of both intrusive and non-intrusive metrics and demonstrates notable strengths in inference efficiency and robustness to unseen tasks. Audio examples are available online (https://vivian556123.github.io/dcem).Comment: Submitted to ICASSP 202

A Novel Tropical Geometry-based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Interactions between all pairs of neighboring trees in 16 forests worldwide reveal details of unique ecological processes in each forest, and provide windows into their evolutionary histories

When Darwin visited the Galapagos archipelago, he observed that, in spite of the islands’ physical similarity, members of species that had dispersed to them recently were beginning to diverge from each other. He postulated that these divergences must have resulted primarily from interactions with sets of other species that had also diverged across these otherwise similar islands. By extrapolation, if Darwin is correct, such complex interactions must be driving species divergences across all ecosystems. However, many current general ecological theories that predict observed distributions of species in ecosystems do not take the details of between-species interactions into account. Here we quantify, in sixteen forest diversity plots (FDPs) worldwide, highly significant negative density-dependent (NDD) components of both conspecific and heterospecific between-tree interactions that affect the trees’ distributions, growth, recruitment, and mortality. These interactions decline smoothly in significance with increasing physical distance between trees. They also tend to decline in significance with increasing phylogenetic distance between the trees, but each FDP exhibits its own unique pattern of exceptions to this overall decline. Unique patterns of between-species interactions in ecosystems, of the general type that Darwin postulated, are likely to have contributed to the exceptions. We test the power of our null-model method by using a deliberately modified data set, and show that the method easily identifies the modifications. We examine how some of the exceptions, at the Wind River (USA) FDP, reveal new details of a known allelopathic effect of one of the Wind River gymnosperm species. Finally, we explore how similar analyses can be used to investigate details of many types of interactions in these complex ecosystems, and can provide clues to the evolution of these interactions

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

The present work was largely supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). The full list of acknowledgments appears in the Supplementary Notes 3 and 4.Peer reviewedPublisher PD

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P <5 x 10(-8), false discovery rate <0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.Peer reviewe

Vampires in the village Žrnovo on the island of Korčula: following an archival document from the 18th century

Središnja tema rada usmjerena je na raščlambu spisa pohranjenog u Državnom arhivu u Mlecima (fond: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) koji se odnosi na događaj iz 1748. godine u korčulanskom selu Žrnovo, kada su mještani – vjerujući da su se pojavili vampiri – oskvrnuli nekoliko mjesnih grobova. U radu se podrobno iznose osnovni podaci iz spisa te rečeni događaj analizira u širem društvenom kontekstu i prate se lokalna vjerovanja.The main interest of this essay is the analysis of the document from the State Archive in Venice (file: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) which is connected with the episode from 1748 when the inhabitants of the village Žrnove on the island of Korčula in Croatia opened tombs on the local cemetery in the fear of the vampires treating. This essay try to show some social circumstances connected with this event as well as a local vernacular tradition concerning superstitions

Machine Learning and Image Processing for Clinical Outcome Prediction: Applications in Medical Data from Patients with Traumatic Brain Injury, Ulcerative Colitis, and Heart Failure

Artificial intelligence (AI) and machine learning (ML) have achieved extensive success in many fields. They are powerful in pattern recognition and function modeling. The digitization of health data provides an important opportunity for improving care delivery and patient management through the AI-based clinical decision-support (CDS) system. Medical images are important components in evaluating the disease severity. While the human’s interpretation of medical images is subjective and qualitative, AI-based models can analyze those data in a more reproducible, quantitative, and less expensive way. With clinical observations and quantitative findings extracted from medical images, ML methods can be used to learn and discover knowledge. The automated CDS system can provide recommendations on diagnosis, treatment, and outcome prediction by leveraging massive medical data. Those systems can facilitate drug development, disease pathology research, and clinical practice. This dissertation investigates medical image analysis and CDS systems development in a more reliable, interpretable manner. Limitations exist in applying AI/ML techniques in medical problems. Medical data may have high variability in terms of the patient population, collection site, equipment, and imaging protocols. It is crucial that the ML and image processing algorithms have a good generalizability and can be reliably applied to unseen patient data. In addition, a broad spectrum of AI/ML methods is among the “black box” models. The lack of justification leads to concerns and hesitations of using AI/ML techniques in clinical or research practice. Features with clinical meaning and models that can be well explained can gain more trust and are more favorable to end-users. In this dissertation, several AI-based CDS systems have been designed and implemented to facilitate clinical and research practice. Novel algorithms are proposed to overcome the challenges of applying AI/ML techniques. To improve the generalizability of the deep learning models, a robust learning algorithm is proposed to encourage the network to be invariant to hematoma intensity variability. A Scale Module and filter pruning technique are proposed to reduce the network’s size and complexity. To improve the interpretability of the CDS systems, a transparent ML algorithm is proposed based on tropical geometry and fuzzy logic, which can learn humanly understandable rules from the dataset and integrate existing domain knowledge to facilitate the model training. Domain knowledge plays an important role in the design of CDS systems. With automated image analysis methods, quantitative and objective measurements are extracted to capture the patient’s condition and disease characteristics in a meaningful and reproducible way. The proposed CDS systems have been validated using data collected from routine practice and clinical trials. The datasets used in this dissertation are from multiple medial centers, which increases the generalizability of the proposed frameworks and trained models. This work aims to research the capacity of AI models toward fully automated CDS systems that can replicate expert judgment and provide insight for the patient. Efforts have been made to improve the generalizability and interpretability of AI/ML models, which are the major limitations that hinder a broad application of AI techniques in practice. The proposed algorithms and strategies in this dissertation leverage big data to improve the healthcare system and disease research. Additionally, the proposed methods are transferable beyond the target application. The contributions of this dissertation have a meaningful impact on applying AI-based systems to clinical and research practice.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/171316/1/hemingy_1.pd