Can students be encouraged to read? Experimental evidence from a large lecture

One of the structural problems of introductory lectures is that students’ learning progress is primarily assessed by taking a final exam. Weekly preparation and reading are driven only by self-motivation. Can a student’s decision to complete her weekly assignments be influenced by a simple reminder? In a pre-registered experimental design, we test if personalised reminders from the instructor delivered via text messages contribute to learning outcomes. We assess formative learning via regular quizzes at the beginning of each class, and summative learning via grades in a final exam. We do not find statistically significant differences in learning outcomes, and discuss how design features potentially drive this result. In the conclusion, we stress the importance of experimental design in assessing innovative and new learning techniques

Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.Comment: For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of databas

CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment

Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users. However, a longstanding challenge in human alignment techniques based on reinforcement learning lies in their inherent complexity and difficulty in training. To address this challenge, we present a simple yet effective Contrastive Learning Framework for Human Alignment (CLHA) to align LLMs with human preferences directly. CLHA employs a novel rescoring strategy to evaluate the noise within the data by considering its inherent quality and dynamically adjusting the training process. Simultaneously, CLHA utilizes pairwise contrastive loss and adaptive supervised fine-tuning loss to adaptively modify the likelihood of generating responses, ensuring enhanced alignment with human preferences. Using advanced methods, CLHA surpasses other algorithms, showcasing superior performance in terms of reward model scores, automatic evaluations, and human assessments on the widely used ``Helpful and Harmless'' dataset

GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians

Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry, and sophisticated interaction with light. This paper presents GaussianHair, a novel explicit hair representation. It enables comprehensive modeling of hair geometry and appearance from images, fostering innovative illumination effects and dynamic animation capabilities. At the heart of GaussianHair is the novel concept of representing each hair strand as a sequence of connected cylindrical 3D Gaussian primitives. This approach not only retains the hair's geometric structure and appearance but also allows for efficient rasterization onto a 2D image plane, facilitating differentiable volumetric rendering. We further enhance this model with the "GaussianHair Scattering Model", adept at recreating the slender structure of hair strands and accurately capturing their local diffuse color in uniform lighting. Through extensive experiments, we substantiate that GaussianHair achieves breakthroughs in both geometric and appearance fidelity, transcending the limitations encountered in state-of-the-art methods for hair reconstruction. Beyond representation, GaussianHair extends to support editing, relighting, and dynamic rendering of hair, offering seamless integration with conventional CG pipeline workflows. Complementing these advancements, we have compiled an extensive dataset of real human hair, each with meticulously detailed strand geometry, to propel further research in this field

CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse

Diversified histone modifications (HMs) are essential epigenetic features. They play important roles in fundamental biological processes including transcription, DNA repair and DNA replication. Chromatin regulators (CRs), which are indispensable in epigenetics, can mediate HMs to adjust chromatin structures and functions. With the development of ChIP-Seq technology, there is an opportunity to study CR and HM profiles at the whole-genome scale. However, no specific resource for the integration of CR ChIP-Seq data or CR-HM ChIP-Seq linkage pairs is currently available. Therefore, we constructed the CR Cistrome database, available online at http://compbio.tongji.edu.cn/cr and http://cistrome.org/cr/, to further elucidate CR functions and CR-HM linkages. Within this database, we collected all publicly available ChIP-Seq data on CRs in human and mouse and categorized the data into four cohorts: the reader, writer, eraser and remodeler cohorts, together with curated introductions and ChIP-Seq data analysis results. For the HM readers, writers and erasers, we provided further ChIP-Seq analysis data for the targeted HMs and schematized the relationships between them. We believe CR Cistrome is a valuable resource for the epigenetics community

Engineering a mevalonate pathway in Halomonas bluephagenesis for the production of lycopene

IntroductionRed-colored lycopene has received remarkable attention in medicine because of its antioxidant properties for reducing the risks of many human cancers. However, the extraction of lycopene from natural hosts is limited. Moreover, the chemically synthesized lycopene raises safety concerns due to residual chemical reagents. Halomonas bluephagenesis is a versatile chassis for the production of fine chemicals because of its open growth property without sterilization.MethodsA heterologous mevalonate (MVA) pathway was introduced into H. bluephagenesis strain TD1.0 to engineer a bacterial host for lycopene production. A pTer7 plasmid mediating the expression of six MVA pathway genes under the control of a phage PMmp1 and an Escherichia coli Ptrc promoters and a pTer3 plasmid providing lycopene biosynthesis downstream genes derived from Streptomyces avermitilis were constructed and transformed into TD1.0. The production of lycopene in the engineered H. bluephagenesis was evaluated. Optimization of engineered bacteria was performed to increase lycopene yield.ResultsThe engineered TD1.0/pTer7-pTer3 produced lycopene at a maximum yield of 0.20 mg/g dried cell weight (DCW). Replacing downstream genes with those from S. lividans elevated the lycopene production to 0.70 mg/g DCW in the TD1.0/pTer7-pTer5 strain. Optimizing the PMmp1 promoter in plasmid pTer7 with a relatively weak Ptrc even increased the lycopene production to 1.22 mg/g DCW. However, the change in the Ptrc promoter in pTer7 with PMmp1 did not improve the yield of lycopene.ConclusionWe first engineered an H. bluephagenesis for the lycopene production. The co-optimization of downstream genes and promoters governing MVA pathway gene expressions can synergistically enhance the microbial overproduction of lycopene

SY18ΔL60L: a new recombinant live attenuated African swine fever virus with protection against homologous challenge

IntroductionAfrican swine fever (ASF) is an acute and highly contagious disease and its pathogen, the African swine fever virus (ASFV), threatens the global pig industry. At present, management of ASF epidemic mainly relies on biological prevention and control methods. Moreover, due to the large genome of ASFV, only half of its genes have been characterized in terms of function.MethodsHere, we evaluated a previously uncharacterized viral gene, L60L. To assess the function of this gene, we constructed a deletion strain (SY18ΔL60L) by knocking out the L60L gene of the SY18 strain. To evaluate the growth characteristics and safety of the SY18ΔL60L, experiments were conducted on primary macrophages and pigs, respectively.ResultsThe results revealed that the growth trend of the recombinant strain was slower than that of the parent strain in vitro. Additionally, 3/5 (60%) pigs intramuscularly immunized with a 105 50% tissue culture infectious dose (TCID50) of SY18ΔL60L survived the 21-day observation period. The surviving pigs were able to protect against the homologous lethal strain SY18 and survive. Importantly, there were no obvious clinical symptoms or viremia.DiscussionThese results suggest that L60L could serve as a virulence- and replication-related gene. Moreover, the SY18ΔL60L strain represents a new recombinant live-attenuated ASFV that can be employed in the development of additional candidate vaccine strains and in the elucidation of the mechanisms associated with ASF infection

Unveiling the Regional Differences and Convergence of Urban Sprawl in China, 2006–2019

There is an obvious imbalanced regional development among eastern, central, and western China. This is also a fundamental problem that policy makers and planners need to address. Specific to urban development, we wondered whether there were regional differences in urban sprawl and whether this trend was under control. By using the urban sprawl index (USI), this paper investigated the spatiotemporal pattern of urban sprawl from 2006 to 2019, and its regional difference and convergence among eastern, central, and western China. It finds that the cities with high, medium, and low sprawl in the east and west regions are distributed with a clear geographical pattern, while the distribution in the central region has no intuitive geographical features. Also, the proportion of cities with high sprawl in the eastern region is more than that in the other regions, with low sprawl in central China and medium sprawl in the western region. Moreover, urban sprawl in all three regions showed a downward trend, but this process was fluctuating and had obvious phase characteristics. It can be concluded that there is a convergence trend in urban sprawl in China over the research period, and the club convergence effect exists in the eastern, central, and western regions

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality