DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation

Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classification task. We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process. It learns an iterative denoising process to `denoise' random depth distribution into a depth map with the guidance of monocular visual conditions. The process is performed in the latent space encoded by a dedicated depth encoder and decoder. Instead of diffusing ground truth (GT) depth, the model learns to reverse the process of diffusing the refined depth of itself into random depth distribution. This self-diffusion formulation overcomes the difficulty of applying generative models to sparse GT depth scenarios. The proposed approach benefits this task by refining depth estimation step by step, which is superior for generating accurate and highly detailed depth maps. Experimental results on KITTI and NYU-Depth-V2 datasets suggest that a simple yet efficient diffusion approach could reach state-of-the-art performance in both indoor and outdoor scenarios with acceptable inference time

Bonus Computing: An Evolution from and a Supplement to Volunteer Computing

Despite the huge success in various worldwide projects, volunteer computing also suffers from the possible lack of computing resources (one volunteered device can join one project at a time) and from the uncertain job interruptions (the volunteered device can crash or disconnect from the Internet at any time). To relieve the challenges faced by volunteer computing, we have proposed bonus computing that exploits the free quotas of public Cloud resources particularly to deal with problems composed of fine-grained, short-running, and compute-intensive tasks. In addition to explaining the loosely-coupled functional architecture and six architectural patterns of bonus computing in this paper, we also employ the Monte-Carlo approximation of Pi (π) as a use case demonstration both to facilitate understanding and to help validate its functioning mechanism. The results exhibit not only effectiveness but also multiple advantages of bonus computing, which makes it a valuable evolution from and supplement to volunteer computing

Reactivating aberrantly hypermethylated p15 gene in leukemic T cells by a phenylhexyl isothiocyanate mediated inter-active mechanism on DNA and chromatin

<p>Abstract</p> <p>Background</p> <p>We have previously demonstrated that phenylhexyl isothiocyanate (PHI), a synthetic isothiocyanate, inhibits histone deacetylases and remodels chromatins to induce growth arrest in HL-60 myeloid leukemia cells in a concentration-dependent manner.</p> <p>Methods</p> <p>To investigate the effect of PHI, a novel histone deacetylases inhibitor (HDACi), on demethylation and activation of transcription of <it>p15 </it>in acute lymphoid leukemia cell line Molt-4, and to further decipher the potential mechanism of demethylation, DNA sequencing and modified methylation specific PCR (MSP) were used to screen <it>p15</it>-M and <it>p15</it>-U mRNA after Molt-4 cells were treated with PHI, 5-Aza and TSA. DNA methyltransferase 1 (DNMT1), 3A (DNMT3A), 3B (DNMT3B) and <it>p15 </it>mRNA were measured by RT-PCR. P15 protein, acetylated histone H3 and histone H4 were detected by Western Blot.</p> <p>Results</p> <p>The gene <it>p15 </it>in Molt-4 cells was hypermethylated and inactive. Hypermethylation of gene <it>p15 </it>was attenuated and <it>p15 </it>gene was activated de novo after 5 days exposure to PHI in a concentration-dependent manner. DNMT1 and DNMT3B were inhibited by PHI (P < 0.05). Alteration of DNMT3A was not significant at those concentrations. Acetylated histone H3 and histone H4 were accumulated markedly after exposure to PHI.</p> <p>Conclusion</p> <p>PHI could induce both DNA demethylation and acetylated H3 and H4 accumulation in Molt-4 cells. Hypermethylation of gene <it>p15 </it>was reversed and <it>p15 </it>transcription could be reactivated de novo by PHI.</p

Constructing Tree-based Index for Efficient and Effective Dense Retrieval

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.Comment: 10 pages, accepted at SIGIR 202

Factors associated with support for smoke-free policies among government workers in six Chinese cities: a cross- sectional study

Background: A certain level of public support for smoke-free environments is a prerequisite for adoption and enforcement of policies and can be used as an indicator of readiness for legislative action. This study assessed support for comprehensive smoke-free policies in a range of settings such as hotels and colleges among government workers in China and identified factors associated with support for smoke-free policies. Understanding the extent to which government workers, a large segment of the working population in China, report a smoke-free workplace and support for smoke-free policies may be important indicators of readiness for strengthened policies given their role in formulating, implementing and enforcing regulations. Methods: Data were from an evaluation of the Tobacco Free Cities initiative of Emory University’s Global Health Institute-China Tobacco Control Partnership. Self-administered surveys were completed by 6,646 workers in 160 government agencies in six Chinese cities. Multivariate logistic regression was used to identify factors associated with support for smoke-free worksites, bars, hotels, and colleges. Results: Over half (54.6%) of participants were male. A large percentage of the male workers smoked (45.9%,) whereas very few women did (1.9%). Fewer than 50% of government workers reported smoke-free policies at work, with 19.0% reporting that smoking is allowed anywhere. Support for smoke-free policies was generally very high, with the lowest levels of support for smoke-free bars (79.0%) and hotels (82.3%), higher levels of support for restaurants (90.0%) and worksites (93.0%), and above 95% support for hospitals, schools, colleges, public transportation and religious settings. Knowledge of the harmfulness of secondhand smoke was positively associated with support for smoke-free policies. Stricter worksite smoking policies were associated with support for smoke-free workplaces and bars, but not hotels and colleges. Women and nonsmokers were more supportive of smoke-free policies in general. Conclusion: Government workers play important roles in formulating, implementing and enforcing regulations; results suggest support for a more comprehensive approach to smoke-free environments in China among workers across a broad range of agencies

FLM-101B: An Open LLM and How to Train It with $100K Budget

Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B

N-Oxide Reduction of Quinoxaline-1,4-Dioxides Catalyzed by

ABSTRACT Quinoxaline-1,4-dioxides (QdNOs) are a class of quinoxaline derivatives that are widely used in humans or animals as drugs or feed additives. However, the metabolic mechanism, especially the involved enzymes, has not been reported in detail. In this study, the N-oxide reduction enzyme, porcine aldehyde oxidase SsAOX1 was identified and characterized. The SsAOX1 gene was cloned from pig liver through reverse-transcription polymerase chain reaction using degenerate primers, which encode a 147-kDa protein with typical aldehyde oxidase motifs, two [2Fe-2S] centers, a flavin adenine dinucleotide (FAD) binding domain, and a molybdenum cofactor domain. After heterologous expression in a prokaryote, purified SsAOX1 formed a functional homodimer under native conditions. Importantly, the SsAOX1 catalyzed the N-oxide reduction at the N1 position of three representative QdNOs (quinocetone, mequindox, and cyadox), which are commonly used as animal feed additives. SsAOX1 has the highest activity toward quinocetone, followed by mequindox and cyadox, with kcat/K m values of 1.94 6 0.04, 1.27 6 0.15, and 0.43 6 0.09 minute 21 mM 21 , respectively. However, SsAOX1 has the lowest substrate affinity for quinocetone, followed by the cyadox and mequindox, with K m values of 4.36 6 0.56, 3.16 6 0.48, and 2.96 6 0.51 mM, respectively. In addition, using site-directed mutagenesis, we found that substitution of glycine 1019 with threonine endows SsAOX1 with N-oxide reductive activity at the N4 position. The goal of this study was to identify and characterize the N-oxide reduction enzyme for a class of veterinary drugs, QdNOs, which will aid in the elucidation of the metabolic pathways of QdNOs and will provide a theoretical basis for their administration and new veterinary drug design

Acid-Base Clusters during Atmospheric New Particle Formation in Urban Beijing

Molecular clustering is the initial step of atmospheric new particle formation (NPF) that generates numerous secondary particles. Using two online mass spectrometers with and without a chemical ionization inlet, we characterized the neutral clusters and the naturally charged ion clusters during NPF periods in urban Beijing. In ion clusters, we observed pure sulfuric acid (SA) clusters, SA-amine clusters, SA-ammonia (NH3) clusters, and SA-amine-NH3 clusters. However, only SA clusters and SA-amine clusters were observed in the neutral form. Meanwhile, oxygenated organic molecule (OOM) clusters charged by a nitrate ion and a bisulfate ion were observed in ion clusters. Acid-base clusters correlate well with the occurrence of sub-3 nm particles, whereas OOM clusters do not. Moreover, with the increasing cluster size, amine fractions in ion acid-base clusters decrease, while NH3 fractions increase. This variation results from the reduced stability differences between SA-amine clusters and SA-NH3 clusters, which is supported by both quantum chemistry calculations and chamber experiments. The lower average number of dimethylamine (DMA) molecules in atmospheric ion clusters than the saturated value from controlled SA-DMA nucleation experiments suggests that there is insufficient DMA in urban Beijing to fully stabilize large SA clusters, and therefore, other basic molecules such as NH3 play an important role.Peer reviewe