PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design

Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large databases can constitute a substantial portion of the overall generation time, particularly when retrievals are periodically performed to align the retrieved content with the latest states of generation. In this paper, we introduce PipeRAG, a novel algorithm-system co-design approach to reduce generation latency and enhance generation quality. PipeRAG integrates (1) pipeline parallelism to enable concurrent retrieval and generation processes, (2) flexible retrieval intervals to maximize the efficiency of pipeline parallelism, and (3) a performance model to automatically balance retrieval quality and latency based on the generation states and underlying hardware. Our evaluation shows that, by combining the three aforementioned methods, PipeRAG achieves up to 2.6$\times$ speedup in end-to-end generation latency while improving generation quality. These promising results showcase the effectiveness of co-designing algorithms with underlying systems, paving the way for the adoption of PipeRAG in future RAG systems

Spontaneous Posterior Segment Vascular Disease Phenotype of a Mouse Model, rnv3, Is Dependent on the Crb1rd8 Allele.

Purpose: To determine the molecular basis of lesion development in a murine model of spontaneous retinal vascularization, rnv3 (retinal vascularization 3, aka JR5558). Methods: Disease progression of rnv3 was examined in longitudinal studies by clinical evaluation, electroretinography (ERG) and light microscopy analyses. The chromosomal position for the recessive rnv3 mutation was determined by DNA pooling and genome-wide linkage analysis. The causative mutation was discovered by comparison of whole exome sequences of rnv3 mutant and wild-type (WT) controls. In order to confirm the causative mutation, transcription activator-like effector nuclease (TALEN)-mediated oligonucleotide directed repair (ODR) was utilized to correct the mutant allele. Phenotypic correction was assessed by fundus imaging and optical coherence tomography of live mice. Results: rnv3 exhibits early-onset, multifocal depigmented retinal lesions observable by fundus examination starting at 18 days of age. The retinal lesions are associated with fluorescein leakage around 25 days of age, with peak leakage at about 4 weeks of age. ERG responses deteriorate as rnv3 mutants age, concomitant with progressive photoreceptor disruption and loss that is observable by histology. Genetic analysis localized rnv3 to mouse chromosome (Chr) 1. By high throughput sequencing of a whole exome capture library of a rnv3/rnv3 mutant and subsequent sequence analysis, a single base deletion (del) in the Crb1 [crumbs family member 1] gene, which was previously reported to cause retinal degeneration 8, was identified. The TALEN-mediated ODR rescued the posterior segment vascularization phenotype; heterozygous Crb1rd8+em1Boc/Crb1rd8 and homozygous Crb1rd8+em1Boc/Crb1rd8+em1Boc mice showed a normal retinal phenotype. Additionally, six novel disruptions of Crb1 that were generated through aberrant non-homologous end joining induced by TALEN exhibited variable levels of vascularization, suggesting allelic effects. Conclusions: The rnv3 model and the models of six novel disruptions of Crb1 are all reliable, novel mouse models for the study of both early and late events associated with posterior segment vascularization and can also be used to test the effects of pharmacological targets for treating human ocular vascular disorders. Further study of these models may provide a greater understanding about how different Crb1 alleles result in aberrant angiogenesis

Deficiency in Lyst function leads to accumulation of secreted proteases and reduced retinal adhesion.

Chediak-Higashi syndrome, caused by mutations in the Lysosome Trafficking Regulator (Lyst) gene, is a recessive hypopigmentation disorder characterized by albinism, neuropathies, neurodegeneration, and defective immune responses, with enlargement of lysosomes and lysosome-related organelles. Although recent studies have suggested that Lyst mutations impair the regulation of sizes of lysosome and lysosome-related organelle, the underlying pathogenic mechanism of Chediak-Higashi syndrome is still unclear. Here we show striking evidence that deficiency in LYST protein function leads to accumulation of photoreceptor outer segment phagosomes in retinal pigment epithelial cells, and reduces adhesion between photoreceptor outer segment and retinal pigment epithelial cells in a mouse model of Chediak-Higashi syndrome. In addition, we observe elevated levels of cathepsins, matrix metallopeptidase (MMP) 3 and oxidative stress markers in the retinal pigment epithelium of Lyst mutants. Previous reports showed that impaired degradation of photoreceptor outer segment phagosomes causes elevated oxidative stress, which could consequently lead to increases of cysteine cathepsins and MMPs in the extracellular matrix. Taken together, we conclude that the loss of LYST function causes accumulation of phagosomes in the retinal pigment epithelium and elevation of several extracellular matrix-remodeling proteases through oxidative stress, which may, in turn, reduce retinal adhesion. Our work reveals previously unreported pathogenic events in the retinal pigment epithelium caused by Lyst deficiency. The same pathogenic events may be conserved in other professional phagocytic cells, such as macrophages in the immune system, contributing to overall Chediak-Higashi syndrome pathology

A missense mutation in Pitx2 leads to early-onset glaucoma via NRF2-YAP1 axis.

Glaucoma is a leading cause of blindness, affecting 70 million people worldwide. Owing to the similarity in anatomy and physiology between human and mouse eyes and the ability to genetically manipulate mice, mouse models are an invaluable resource for studying mechanisms underlying disease phenotypes and for developing therapeutic strategies. Here, we report the discovery of a new mouse model of early-onset glaucoma that bears a transversion substitution c. G344T, which results in a missense mutation, p. R115L in PITX2. The mutation causes an elevation in intraocular pressure (IOP) and progressive death of retinal ganglion cells (RGC). These ocular phenotypes recapitulate features of pathologies observed in human glaucoma. Increased oxidative stress was evident in the inner retina. We demonstrate that the mutant PITX2 protein was not capable of binding to Nuclear factor-like 2 (NRF2), which regulates Pitx2 expression and nuclear localization, and to YAP1, which is necessary for co-initiation of transcription of downstream targets. PITX2-mediated transcription of several antioxidant genes were also impaired. Treatment with N-Acetyl-L-cysteine exerted a profound neuroprotective effect on glaucoma-associated neuropathies, presumably through inhibition of oxidative stress. Our study demonstrates that a disruption of PITX2 leads to glaucoma optic pathogenesis and provides a novel early-onset glaucoma model that will enable elucidation of mechanisms underlying the disease as well as to serve as a resource to test new therapeutic strategies

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Dietary therapy to improve nutrition and gut health in paediatric Crohn’s disease; a feasibility study

Bovine colostrum (BC) has anti-inflammatory, anti-infective, growth and intestinal repair factors that may be beneficial in Crohn’s disease (CD). We assessed whether daily BC for up to 3 months was acceptable to children and young people (CYP) with CD in remission or of mild/moderate severity. CYP were randomised to receive either BC or matching placebo milk daily for 6 weeks (blinded phase); all received BC for the following 6 weeks (open phase). In 23 CYP, median (inter-quartile range) age was 15.2 (13.9–16.1) years and 9 (39.1%) were girls. A similar proportion of CYP in the BC and placebo arms completed the blinded phase (8/12, 75.0% and 9/11, 81.8% respectively). Twelve (70.6%) CYP completed the open phase with 7 (58.3%) tolerating BC for 3 months. Diaries in weeks 2, 6 and 12 revealed that most CYP took BC every day (5/7, 71.4%; 5/8, 62,5% and 6/11, 54.5% respectively). In interviews, opinions were divided as to preference of BC over the placebo milk and some preferred BC over other nutritional supplements. Symptoms, clinical and laboratory variables and quality of life were similar in the two arms. BC may be an acceptable nutritional supplement for daily, longer-term use in CYP with CD

Neural forecasting: Introduction and literature overview

Neural network based forecasting methods have become ubiquitous in large-scale industrial forecasting applications over the last years. As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also reached academia. This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning. Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.Comment: 66 pages, 5 figure