RLIPv2: Fast Scaling of Relational Language-Image Pre-training

Relational Language-Image Pre-training (RLIP) aims to align vision representations with relational texts, thereby advancing the capability of relational reasoning in computer vision tasks. However, hindered by the slow convergence of RLIPv1 architecture and the limited availability of existing scene graph data, scaling RLIPv1 is challenging. In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data. To enable fast scaling, RLIPv2 introduces Asymmetric Language-Image Fusion (ALIF), a mechanism that facilitates earlier and deeper gated cross-modal fusion with sparsified language encoding layers. ALIF leads to comparable or better performance than RLIPv1 in a fraction of the time for pre-training and fine-tuning. To obtain scene graph data at scale, we extend object detection datasets with free-form relation labels by introducing a captioner (e.g., BLIP) and a designed Relation Tagger. The Relation Tagger assigns BLIP-generated relation texts to region pairs, thus enabling larger-scale relational pre-training. Through extensive experiments conducted on Human-Object Interaction Detection and Scene Graph Generation, RLIPv2 shows state-of-the-art performance on three benchmarks under fully-finetuning, few-shot and zero-shot settings. Notably, the largest RLIPv2 achieves 23.29mAP on HICO-DET without any fine-tuning, yields 32.22mAP with just 1% data and yields 45.09mAP with 100% data. Code and models are publicly available at https://github.com/JacobYuan7/RLIPv2.Comment: Accepted to ICCV 2023. Code and models: https://github.com/JacobYuan7/RLIPv

'With this study, we have hope that something is coming': community members' perceptions of HIV cure-related research in Durban, South Africa - a qualitative focus group study

BACKGROUND: Developing a cure for HIV remains a global scientific priority. In 2022, the Females Rising through Education, Support and Health (FRESH) cohort launched an HIV cure-related trial involving an analytical treatment interruption (ATI) in Durban, South Africa. OBJECTIVES: To explore community perspectives about HIV cure-related research. METHODS: Between July-August 2022, we conducted three focus groups with community members. We transcribed audio recordings verbatim and used content analysis to analyze the data. RESULTS: Twenty community members (13 women and 7 men) participated in three focus groups (HIV status not included). Participants viewed HIV cure-related research as a way to address the issue of defaulting on (not taking) HIV treatment. Participants expressed hesitancy around ATIs, since these contradict longstanding treatment adherence messages. Participants shared concerns around the risk of side effects from experimental interventions balanced against potential efficacy. They advocated for trial participants to have the right to decide whether to inform their sex partners about their HIV status and ATI participation, rather than research teams making disclosure mandatory. Focus group participants also emphasized the importance of using simple language to explain HIV cure-related research. CONCLUSIONS: With HIV cure trials set to launch across Africa in the future, there is a critical need to better understand and respond to local community needs and preferences and to adopt this as standard practice prior to regional trial implementation

Basaltic and Solution Reference Materials for Iron, Copper and Zinc Isotope Measurements

Iron, Cu and Zn stable isotope systems are applied in constraining a variety of geochemical and environmental processes. Secondary reference materials have been developed by the Institute of Geology, Chinese Academy of Geological Sciences (CAGS), in collaboration with other participating laboratories, comprising three solutions (CAGS-Fe, CAGS-Cu and CAGS-Zn) and one basalt (CAGS-Basalt). These materials exhibit sufficient homogeneity and stability for application in Fe, Cu and Zn isotopic ratio determinations. Reference values were determined by inter-laboratory analytical comparisons involving up to eight participating laboratories employing MC-ICP-MS techniques, based on the unweighted means of submitted results. Isotopic compositions are reported in per mil notation, based on reference materials IRMM-014 for Fe, NIST SRM 976 for Cu and IRMM-3702 for Zn. Respective reference values of CAGS-Fe, CAGS-Cu and CAGS-Zn solutions are as follows: δ56Fe = 0.83 ± 0.06 and δ57Fe = 1.20 ± 0.12, δ65Cu = 0.57 ± 0.05, and δ66Zn = -0.79 ± 0.12 and δ68Zn = -1.65 ± 0.24, respectively. Those of CAGS-Basalt are δ56Fe = 0.15 ± 0.05, δ57Fe = 0.22 ± 0.05, δ65Cu = 0.12 ± 0.07, δ66Zn = 0.17 ± 0.11, and δ68Zn = 0.34 ± 0.21 (2s)

Functional Identification of Neuroprotective Molecules

The central nervous system has the capacity to activate profound neuroprotection following sub-lethal stress in a process termed preconditioning. To gain insight into this potent survival response we developed a functional cloning strategy that identified 31 putative neuroprotective genes of which 28 were confirmed to provide protection against oxygen-glucose deprivation (OGD) or excitotoxic exposure to N-methyl-D-aspartate (NMDA) in primary rat cortical neurons. These results reveal that the brain possesses a wide and diverse repertoire of neuroprotective genes. Further characterization of these and other protective signals could provide new treatment opportunities for neurological injury from ischemia or neurodegenerative disease

Three-dimensional photographic analysis of the face in European adults from southern Spain with normal occlusion: reference anthropometric measurements

Background: Recent non-invasive 3D photography method has been applied to facial analysis, offering numerous advantages in orthodontic. The purpose of this study was to analyze the faces of a sample of healthy European adults from southern Spain with normal occlusion in order to establish reference facial soft tissue anthropometric parameters in this specific geographic-ethnic population, as well as to analyze sexual dimorphism. Methods: A sample of 100 healthy adult volunteers consisting of 50 women (mean age, 22.92 ± 1.56 years) and 50 men (mean age, 22.37 ± 2.12 years) were enrolled in this study. All participants had normal occlusion, skeletal Class I, mesofacial pattern, and healthy body mass index. Three-dimensional photographs of the faces were captured noninvasively using Planmeca ProMax 3D ProFace®. Thirty landmarks related to the face, eyes, nose, and orolabial and chin areas were identified. Results: Male displayed higher values in all vertical and transversal dimensions, with the exception of the lower lip height. Larger differences between sexes were observed in face, mandible, and nose. Male also had higher values in the angular measurements which referred to the nose. No sex differences were found in transverse upper lip prominence or transverse mandibular prominence. No differences were found in the ratio measurements, with the exception of intercantal width/nasal width, which was higher in women than in men. Conclusions: Reference anthropometric measurements of facial soft tissues have been established in European adults from southern Spain with normal occlusion. Significant sexual dimorphism was found, with remarkable differences in size between sexe

Transcriptional and Post-Transcriptional Mechanisms for Oncogenic Overexpression of Ether À Go-Go K+ Channel

The human ether-à-go-go-1 (h-eag1) K+ channel is expressed in a variety of cell lines derived from human malignant tumors and in clinical samples of several different cancers, but is otherwise absent in normal tissues. It was found to be necessary for cell cycle progression and tumorigenesis. Specific inhibition of h-eag1 expression leads to inhibition of tumor cell proliferation. We report here that h-eag1 expression is controlled by the p53−miR-34−E2F1 pathway through a negative feed-forward mechanism. We first established E2F1 as a transactivator of h-eag1 gene through characterizing its promoter region. We then revealed that miR-34, a known transcriptional target of p53, is an important negative regulator of h-eag1 through dual mechanisms by directly repressing h-eag1 at the post-transcriptional level and indirectly silencing h-eag1 at the transcriptional level via repressing E2F1. There is a strong inverse relationship between the expression levels of miR-34 and h-eag1 protein. H-eag1antisense antagonized the growth-stimulating effects and the upregulation of h-eag1 expression in SHSY5Y cells, induced by knockdown of miR-34, E2F1 overexpression, or inhibition of p53 activity. Therefore, p53 negatively regulates h-eag1 expression by a negative feed-forward mechanism through the p53−miR-34−E2F1 pathway. Inactivation of p53 activity, as is the case in many cancers, can thus cause oncogenic overexpression of h-eag1 by relieving the negative feed-forward regulation. These findings not only help us understand the molecular mechanisms for oncogenic overexpression of h-eag1 in tumorigenesis but also uncover the cell-cycle regulation through the p53−miR-34−E2F1−h-eag1 pathway. Moreover, these findings place h-eag1 in the p53−miR-34−E2F1−h-eag1 pathway with h-eag as a terminal effecter component and with miR-34 (and E2F1) as a linker between p53 and h-eag1. Our study therefore fills the gap between p53 pathway and its cellular function mediated by h-eag1

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5