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Text-to-3D content creation is a rapidly evolving research area. Given the scarcity of 3D data, current approaches often adapt pre-trained 2D diffusion models for 3D synthesis. Among these approaches, Score Distillation Sampling (SDS) has been widely adopted. However, the issue of over-smoothing poses a significant limitation on the high-fidelity generation of 3D models. To address this challenge, LucidDreamer replaces the Denoising Diffusion Probabilistic Model (DDPM) in SDS with the Denoising Diffusion Implicit Model (DDIM) to construct Interval Score Matching (ISM). However, ISM inevitably inherits inconsistencies from DDIM, causing reconstruction errors during the DDIM inversion process. This results in poor performance in the detailed generation of 3D objects and loss of content. To alleviate these problems, we propose a novel method named Exact Score Matching (ESM). Specifically, ESM leverages auxiliary variables to mathematically guarantee exact recovery in the DDIM reverse process. Furthermore, to effectively capture the dynamic changes of the original and auxiliary variables, the LoRA of a pre-trained diffusion model implements these exact paths. Extensive experiments demonstrate the effectiveness of ESM in text-to-3D generation, particularly highlighting its superiority in detailed generation.

With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence. Sora, developed by OpenAI, which is capable of minute-level world-simulative abilities can be considered as a milestone on this developmental path. However, despite its notable successes, Sora still encounters various obstacles that need to be resolved. In this survey, we embark from the perspective of disassembling Sora in text-to-video generation, and conducting a comprehensive review of literature, trying to answer the question, \textit{From Sora What We Can See}. Specifically, after basic preliminaries regarding the general algorithms are introduced, the literature is categorized from three mutually perpendicular dimensions: evolutionary generators, excellent pursuit, and realistic panorama. Subsequently, the widely used datasets and metrics are organized in detail. Last but more importantly, we identify several challenges and open problems in this domain and propose potential future directions for research and development., Comment: A comprehensive list of text-to-video generation studies in this survey is available at https://github.com/soraw-ai/Awesome-Text-to-Video-Generation

Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we pioneer an ensemble knowledge distillation approach and apply it on MLFFs to improve the stability of MD simulations. Finally, we propose the density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity across various solvents and salt combinations. Our current model, trained on more than 15 chemical species, achieves the average density error of 0.01 g/cm$^3$ on various compositions compared with experimental data. Moreover, our model demonstrates transferability to molecules not included in the quantum mechanical dataset. We envision this work as paving the way to a "universal MLFF" capable of simulating properties of common organic liquids.

Antisense oligonucleotide (ASO) is a novel therapeutic platform for targeted cancer therapy. Previously, we have demonstrated that miR-146b-5p plays an important role in colorectal cancer progression. However, a safe and effective strategy for delivery of an ASO to its targeted RNA remains as a major hurdle in translational advances. Human umbilical cord mesenchymal cell (hUC-MSC)-derived exosomes were used as vehicles to deliver an anti-miR-146b-5p ASO (PMO-146b). PMO-146b was assembled onto the surface of exosomes (e) through covalent conjugation to an anchor peptide CP05 (P) that recognized an exosomal surface marker, CD63, forming a complex named ePPMO-146b. After ePPMO-146b treatment, cell proliferation, uptake ability, and migration assays were performed, and epithelial-mesenchymal transition progression was evaluated in vitro. A mouse xenograft model was used to determine the antitumor effect and distribution of ePPMO-146b in vivo. ePPMO-146b was taken up by SW620 cells and effectively inhibited cell proliferation and migration. The conjugate also exerted antitumor efficacy in a xenograft mouse model of colon cancer by systematic administration, where PPMO-146b was enriched in tumor tissue. Our study highlights the potential of hUC-MSC-derived exosomes anchored with PPMO-146b as a novel safe and effective approach for PMO backboned ASO delivery.Graphical AbstractSchematic illustration of the preparation of an exosomal anchor peptide (CP05)-PMO that conjugately binds to exosomes from hUC-MSCs (ePPMO-146b) and the antitumor effect of ePPMO-146b in CRC, which occurs through the inhibition of Smad signaling and epithelial-mesenchymal transition.

Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed. Nevertheless, as one of the mainstream, existing adversarial-based methods neglect to filter the irrelevant semantic knowledge, hindering adaptation performance improvement. Besides, they require an additional domain discriminator that strives extractor to generate confused representations, but discrete designing may cause model collapse. To tackle the above issues, we propose Crucial Semantic Classifier-based Adversarial Learning (CSCAL), which pays more attention to crucial semantic knowledge transferring and leverages the classifier to implicitly play the role of domain discriminator without extra network designing. Specifically, in intra-class-wise alignment, a Paired-Level Discrepancy (PLD) is designed to transfer crucial semantic knowledge. Additionally, based on classifier predictions, a Nuclear Norm-based Discrepancy (NND) is formed that considers inter-class-wise information and improves the adaptation performance. Moreover, CSCAL can be effortlessly merged into different UDA methods as a regularizer and dramatically promote their performance.

Deep learning-based Computer-Aided Diagnosis (CAD) has attracted appealing attention in academic researches and clinical applications. Nevertheless, the Convolutional Neural Networks (CNNs) diagnosis system heavily relies on the well-labeled lesion dataset, and the sensitivity to the variation of data distribution also restricts the potential application of CNNs in CAD. Unsupervised Domain Adaptation (UDA) methods are developed to solve the expensive annotation and domain gaps problem and have achieved remarkable success in medical image analysis. Yet existing UDA approaches only adapt knowledge learned from the source lesion domain to a single target lesion domain, which is against the clinical scenario: the new unlabeled target domains to be diagnosed always arrive in an online and continual manner. Moreover, the performance of existing approaches degrades dramatically on previously learned target lesion domains, due to the newly learned knowledge overwriting the previously learned knowledge (i.e., catastrophic forgetting). To deal with the above issues, we develop a meta-adaptation framework named Consecutive Lesion Knowledge Meta-Adaptation (CLKM), which mainly consists of Semantic Adaptation Phase (SAP) and Representation Adaptation Phase (RAP) to learn the diagnosis model in an online and continual manner. In the SAP, the semantic knowledge learned from the source lesion domain is transferred to consecutive target lesion domains. In the RAP, the feature-extractor is optimized to align the transferable representation knowledge across the source and multiple target lesion domains.

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by modeling the original dialog to the graph-structured dialog and leveraging the graph neural network to learn the contextual information; 3) Synthesize the bug reports by utilizing the TextCNN model and Transfer Learning network to classify the sentences into three groups: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 74.21%, improving the best baseline by 10.37%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 67.37%, 87.14%, and 65.03%, improving the best baselines by 7.21%, 7.38%, 5.30%, respectively. A human evaluation also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.

Zhang Y, Zhou J, Yang C, et al. Int J Nanomedicine. 2016;11:1119–1130. The authors have advised due to an error at the time of figure assembly, Figure 4 on page 1124 is incorrect. The correct Figure 4 is shown in Download Article. Page 1126, Table 1, the authors have advised a mistake was made in calculating the Drug loading content (DLC) in the manuscript. The correct Table 1 is shown in Download Article. The authors apologize for these errors and advise it does not affect the results of the paper. Read the original article

Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%., Comment: 13 pages, 5 figures

Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is importantly different from uplift models. To address this, we introduce a set of feature selection methods explicitly designed for uplift modeling, drawing inspiration from statistics and information theory. We conduct empirical evaluations on the proposed methods on publicly available datasets, demonstrating the advantages of the proposed methods compared to traditional feature selection. We make the proposed methods publicly available as a part of the CausalML open-source package.

Die vorliegende Arbeit beschäftigt sich mit der Interpretation vier ausgewählter Filme des Filmemachers Ang Lee – Pushing Hands, The Wedding Banquet, Brokeback Mountain und Hulk, die in den Jahren 1992 bis 2005 entstanden. Sie erforscht die unterschiedliche Inszenierung von Konzepten der Männlichkeit im chinesischen und US-amerikanischen Kontext, sowie den Männlichkeitsdiskurs in Räumen des kulturellen Üergangs. Die Untersuchung Lees männlicher Figuren und Männlichkeitskonzepte macht sich sowohl die chinesische als auch die westliche erkenntnistheoretische Perspektive zu eigen, dabei ist Untersuchung sowohl konzeptionell als auch analytisch angelegt. Auf der konzeptionellen Ebene soll sie zeigen, wie sich die Konstruktion von Männlichkeitskonzepten unter der Einbeziehung nicht nur der westlichen konzeptionellen Argumentation von transkulturellen Räumen (Transdifferenz), sondern auch von andersartigen erkenntnistheoretischen Perspektiven, hier der chinesischen, besser erklären lässt. Auf der analytischen Ebene werden in der Untersuchung der Inszenierung männlicher Figuren audio-visuelle Textanalysen benutzt. Die Analyse hat deutlich die Komplexität und Vielfältigkeit der Aushandlung von Männlichkeitskonzepten in transkulturellen Räumen gezeigt, wobei die Rekonstruktion und die Neuverhandlung von Männlichkeit sowohl emanzipatorisch als auch repressiv von statten gehen kann. Männliche Protagonisten bei Lee finden drei unterschiedliche Wege, ihre männliche Identität zu konstruieren. Als erste Lösung unterdrücken sie den transdifferenten Aspekt und wählen die klare Zugehörigkeit zu einer der Kulturen, die dann als Ursprung für die Restauration der Männlichkeit dient. Die zweite Lösung ist das Annehmen der Transdifferenz um eine mehrdeutige maskuline Identität im transkulturellen Raum aufzubauen. Als letzte Lösung gelingt es einen männlichen Figuren, kulturelle Grenzen zu überschreiten und eine transkulturelle Männlichkeit zu manifestieren., This dissertation consists of readings of four selected films by Ang Lee — Pushing Hands, The Wedding Banquet, Brokeback Mountain and Hulk, ranging over a decade from 1992 to 2005. It explores Lee’s representation of diverse Chinese and American masculinities, discussing negotiations of masculinities in transcultural spaces. My exploration of Lee’s representation of men and masculinities is equipped with double epistemological perspectives, namely, both Chinese and Western. My project is both conceptual and analytical. On the conceptual level, I intend to demonstrate how constructions of masculinities can be more productively explained by employing not only the Western conceptual arguments of transcultural space (transdifference) but also by reading this space from different epistemological perspectives, namely the Western and Chinese. On the analytical level, I employ audio and visual textual analysis in my examination of Lee’s portrayal of male figures. My analysis has clearly demonstrated the complexity and multiplicity in negotiations of masculinities in transcultural spaces, which can be both emancipatory and repressive in re-constructing and re-negotiating one’s masculinity. Male subjectivities in Lee’s films turn to three different ways to construct or reconstruct their manliness. First, men suppress trandifference and opt for a clear belonging to a certain culture, in particular, the culture of origin for masculinity restoration. Second, men embrace transidifference to construct an ambiguous masculine identity in transcultural spaces. Third, men might transcend cultural boundaries to demonstrate transcultural manhood.

Yumin Zhang, 1,* Junhui Zhou, 2,* Cuihong Yang, 1 Weiwei Wang, 3 Liping Chu, 1 Fan Huang, 1 Qiang Liu, 1 Liandong Deng, 2 Deling Kong, 3 Jianfeng Liu, 1 Jinjian Liu 1 1Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Science and Peking Union Medical College, 2Department of Polymer Science and Technology, School of Chemical Engineering and Technology, Tianjin University, 3Tianjin Key Laboratory of Biomaterial Research, Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, People’s Republic of China *These authors contributed equally in this work Abstract: Although the shortcomings of small molecular antitumor drugs were efficiently improved by being entrapped into nanosized vehicles, premature drug release and insufficient tumor targeting demand innovative approaches that boost the stability and tumor responsiveness of drug-loaded nanocarriers. Here, we show the use of the core cross-linking method to generate a micelle with enhanced drug encapsulation ability and sensitivity of drug release in tumor. This kind of micelle could increase curcumin (Cur) delivery to HeLa cells in vitro and improve tumor accumulation in vivo. We designed and synthesized the core cross-linked micelle (CCM) with polyethylene glycol and folic acid-polyethylene glycol as the hydrophilic units, pyridyldisulfide as the cross-linkable and hydrophobic unit, and disulfide bond as the cross-linker. CCM showed spherical shape with a diameter of 91.2 nm by the characterization of dynamic light scattering and transmission electron microscope. Attributed to the core cross-linking, drug-loaded CCM displayed higher Nile Red or Cur-encapsulated stability and better sensitivity to glutathione than noncross-linked micelle (NCM). Cellular uptake and in vitro antitumor studies proved the enhanced endocytosis and better cytotoxicity of

Jianfeng Liu, Jinjian Liu, Hongyan Xu, Yumin Zhang, Liping Chu, Qingfen Liu, Naling Song, Cuihong YangTianjin Key Laboratory of Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Science and Peking Union Medical College, People's Republic of ChinaAbstract: The poor aqueous solubility and low bioavailability of curcumin restrict its clinical application for cancer treatment. In this study, a novel tumor-targeting nanofiber carrier was developed to improve the solubility and tumor-targeting ability of curcumin using a self-assembled Nap-GFFYG-RGD peptide. The morphologies of the peptide nanofiber and the curcumin-encapsulated nanofiber were visualized by transmission electron microscopy. The tumor-targeting activity of the curcumin-encapsulated Nap-GFFYG-RGD peptide nanofiber (f-RGD-Cur) was studied in vitro and in vivo, using Nap-GFFYG-RGE peptide nanofiber (f-RGE-Cur) as the control. Curcumin was encapsulated into the peptide nanofiber, which had a diameter of approximately 10–20 nm. Curcumin showed sustained-release behavior from the nanofibers in vitro. f-RGD-Cur showed much higher cellular uptake in αvβ3 integrin-positive HepG2 liver carcinoma cells than did non-targeted f-RGE-Cur, thereby leading to significantly higher cytotoxicity. Ex vivo studies further demonstrated that curcumin could accumulate markedly in mouse tumors after administration of f-RGD-Cur via the tail vein. These results indicate that Nap-GFFYG-RGD peptide self-assembled nanofibers are a promising hydrophobic drug delivery system for targeted treatment of cancer.Keywords: nanofiber, tumor-targeting, self-assembling, curcumin, drug delivery

This thesis starts with a literature review, outlining the major issues identified in the literature concerning virtual manufacturing enterprise (VME) transformation. Then it details the research methodology used – a systematic approach for empirical research. next, based on the conceptual framework proposed, this thesis builds three modules to form a reference model, with the purpose of clarifying the important issues relevant to transforming a traditional manufacturing company into a VME. The first module proposes a mechanism of VME transformation – operating along the VME metabolism. The second module builds a management function within a VME to ensure a proper operation of the mechanism. This function helps identify six areas as closely related to VME transformation: lean manufacturing; competency protection; internal operation performance measurement; alliance performance measurement; knowledge management; alliance decision making. The third module continues and proposes an alliance performance measurement system which includes 14 categories of performance indicators. An analysis template for alliance decision making is also proposed and integrated into the first module. To validate these three modules, 7 manufacturing organisations (5 in China and 2 in the UK) were investigated, and these field case studies are analysed in this thesis. The evidence found in these organisations, together with the evidence collected from the literature, including both researcher views and literature case studies, provide support for triangulation evidence. In addition, this thesis identifies the strength and weakness patterns of the manufacturing companies within the theoretical niche of this research, and clarifies the relationships among some major research areas from the perspective of virtual manufacturing. Finally, the research findings are summarised, as well as their theoretical and practical implications. Research limitations and recommendations for future work conclude

The SL-type continuous centrifugal separator with injection-flowing film centrifuging (IFFC) for separating fine and ultrafine mineral fractions has been recently developed in China and is capable of recovering particles down to 1 micron in size. The principles of separation are discussed. IFFC is a transient pulsating film process which combines thin film jigging and thin film concentration functions. It cannot only intensify the looseness and stratification of settled particles, but can also create a hydraulic weir effect to improve recovery of fines and ultrafines and significantly improve the performance of thin film separation. In the Chehe tin concentrator of the Dachang Tin Mine company, Guangxi, China, 2 SL separators have been applied to treat cassiterite slime (past tailings) and to replace the old tin flotation system. Plant results show that 50-60% cassiterite can be recovered from minus 10 micron slime with an enrichment ratio of 10 to 12. Particles down to 1 micron were recovered. Under optimal conditions, the recovery from 10 to 74 micron slime can reach more than 85%., The SL-type continuous centrifugal separator with injection-flowing film centrifuging (IFFC) for separating fine and ultrafine mineral fractions has been recently developed in China and is capable of recovering particles down to 1 micron in size. The principles of separation are discussed. IFFC is a transient pulsating film process which combines thin film jigging and thin film concentration functions. It cannot only intensify the looseness and stratification of settled particles, but can also create a hydraulic weir effect to improve recovery of fines and ultrafines and significantly improve the performance of thin film separation. In the Chehe tin concentrator of the Dachang Tin Mine company, Guangxi, China, 2 SL separators have been applied to treat cassiterite slime (past tailings) and to replace the old tin flotation system. Plant results show that 50-60% cassiterite can be recovered from minus 10 micron slime with an enrichment ratio of 10 to 12. Particles down to 1 micron were recovered. Under optimal conditions, the recovery from 10 to 74 micron slime can reach more than 85%.

Thermodynamic modelling of the Si-P system was carried out and the dephosphorisation reaction equilibria between Si and CaO-based slags under oxidising and reducing conditions were then calculated using thermodynamic databases for Si alloy and slags. The results show that only dephosphorisation with the formation of phosphate occurs under oxidising conditions in molten CaO-based slag as the Gibbs free energy of the reaction is strongly positive. Reducing dephosphorisation with the formation of phosphide may be applied but is not so effective as Ca dissolution in molten Si can occur requiring further solvent refining or acid leaching., Thermodynamic modelling of the Si-P system was carried out and the dephosphorisation reaction equilibria between Si and CaO-based slags under oxidising and reducing conditions were then calculated using thermodynamic databases for Si alloy and slags. The results show that only dephosphorisation with the formation of phosphate occurs under oxidising conditions in molten CaO-based slag as the Gibbs free energy of the reaction is strongly positive. Reducing dephosphorisation with the formation of phosphide may be applied but is not so effective as Ca dissolution in molten Si can occur requiring further solvent refining or acid leaching.