Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, variational auto-encoders, are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require to use reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the UCI repository

Connecting With Fundamental Mathematical Knowledge Directly: The Organizational Features of Good Mathematical Cognitive Structure

This paper reported on the study of a good mathematical cognitive structure (GMCS) based on 43 top university students and 82 concepts of Calculus materials, using the social network analysis method. The results indicated that the GMCS has the following organizational features: (1) The mathematical knowledge (MK) in GMCS interconnected widely, especially in MK with a higher connection tightness; (2) Most connections between MK were direct; (3) MK of the basic and higher inclusive level had a greater impact; and (4) There were multiple MK accumulation points connecting others to form subsets. These new findings enrich the results of previous GMCS studies and promotes further exploration of GMCS. In view of this, teachers should pay closer attention to basic and abstract MK and help their students construct various direct connections of the MK in their mind

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average since there is no specific knowledge in it. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, which is about Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, which is not available for general LLM, so it is well suited for evaluating methods aimed at improving domain-specific capabilities of LLM. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our model fusion framework outperforms the commonly used LLM with retrieval methods.Comment: 13 pages, 1 figur

Self-Guard: Empower the LLM to Safeguard Itself

The jailbreak attack can bypass the safety measures of a Large Language Model (LLM), generating harmful content. This misuse of LLM has led to negative societal consequences. Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards. Safety training focuses on further training LLM to enhance its safety. On the other hand, safeguards involve implementing external models or filters to prevent harmful outputs. However, safety training has constraints in its ability to adapt to new attack types and often leads to a drop in model performance. Safeguards have proven to be of limited help. To tackle these issues, we propose a novel approach called Self-Guard, which combines the strengths of both safety methods. Self-Guard includes two stages. In the first stage, we enhance the model's ability to assess harmful content, and in the second stage, we instruct the model to consistently perform harmful content detection on its own responses. The experiment has demonstrated that Self-Guard is robust against jailbreak attacks. In the bad case analysis, we find that LLM occasionally provides harmless responses to harmful queries. Additionally, we evaluated the general capabilities of the LLM before and after safety training, providing evidence that Self-Guard does not result in the LLM's performance degradation. In sensitivity tests, Self-Guard not only avoids inducing over-sensitivity in LLM but also can even mitigate this issue

The Invasive MED/Q \u3cem\u3eBemisia tabaci\u3c/em\u3e Genome: A Tale of Gene Loss and Gene Gain

Background: Sweetpotato whitefly, Bemisia tabaci MED/Q and MEAM1/B, are two economically important invasive species that cause considerable damages to agriculture crops through direct feeding and indirect vectoring of plant pathogens. Recently, a draft genome of B. tabaci MED/Q has been assembled. In this study, we focus on the genomic comparison between MED/Q and MEAM1/B, with a special interest in MED/Q’s genomic signatures that may contribute to the highly invasive nature of this emerging insect pest. Results: The genomes of both species share similarity in syntenic blocks, but have significant divergence in the gene coding sequence. Expansion of cytochrome P450 monooxygenases and UDP glycosyltransferases in MED/Q and MEAM1/B genome is functionally validated for mediating insecticide resistance in MED/Q using in vivo RNAi. The amino acid biosynthesis pathways in MED/Q genome are partitioned among the host and endosymbiont genomes in a manner distinct from other hemipterans. Evidence of horizontal gene transfer to the host genome may explain their obligate relationship. Putative loss-of-function in the immune deficiency-signaling pathway due to the gene loss is a shared ancestral trait among hemipteran insects. Conclusions: The expansion of detoxification genes families, such as P450s, may contribute to the development of insecticide resistance traits and a broad host range in MED/Q and MEAM1/B, and facilitate species’ invasions into intensively managed cropping systems. Numerical and compositional changes in multiple gene families (gene loss and gene gain) in the MED/Q genome sets a foundation for future hypothesis testing that will advance our understanding of adaptation, viral transmission, symbiosis, and plant-insect-pathogen tritrophic interactions

Diffusion basis spectrum imaging measures anti-inflammatory and neuroprotective effects of fingolimod on murine optic neuritis

OBJECTIVE: To prospectively determine whether diffusion basis spectrum imaging (DBSI) detects, differentiates and quantitates coexisting inflammation, demyelination, axonal injury and axon loss in mice with optic neuritis (ON) due to experimental autoimmune encephalomyelitis (EAE), and to determine if DBSI accurately measures effects of fingolimod on underlying pathology. METHODS: EAE was induced in 7-week-old C57BL/6 female mice. Visual acuity (VA) was assessed daily to detect onset of ON after which daily oral-treatment with either fingolimod (1 mg/kg) or saline was given for ten weeks. In vivo DBSI scans of optic nerves were performed at baseline, 2-, 6- and 10-weeks post treatment. DBSI-derived metrics including restricted isotropic diffusion tensor fraction (putatively reflecting cellularity), non-restricted isotropic diffusion tensor fraction (putatively reflecting vasogenic edema), DBSI-derived axonal volume, axial diffusivity, λ RESULTS: Optic nerves of fingolimod-treated mice exhibited significantly better (p \u3c 0.05) VA than saline-treated group at each time point. During ten-week of treatment, DBSI-derived non-restricted and restricted-isotropic-diffusion-tensor fractions, and axonal volumes were not significantly different (p \u3e 0.05) from the baseline values in fingolimod-treated mice. Transient DBSI-λ CONCLUSION: DBSI was used to assess changes of the underlying optic nerve pathologies in EAE mice with ON, exhibiting great potential as a noninvasive outcome measure for monitoring disease progression and therapeutic efficacy for MS

Amino Acid Utilization May Explain Why Bemisia tabaci Q and B Differ in Their Performance on Plants Infected by the Tomato yellow leaf curl virus

To make plants more attractive to vectors of viruses, plant-infecting viruses can alter host plant physiology. The recent outbreaks of Tomato yellow leaf curl virus (TYLCV) relate to the spread of its primary vector, the whitefly Bemisia tabaci. Here, we investigated the question of whether the better performance of B. tabaci Q, relative to that of the B biotype, on TYLCV-infected tomato plants could be explained by differences in the ability of the B. tabaci Q and B to obtain free amino acids from the virus-infected plants. We found that the TYLCV infection of tomato plants significantly affected the mole percentage (mol%) of free amino acids in the phloem sap of the tomato plants and the mol% of free amino acids in B. tabaci adults and B. tabaci honeydew. The TYLCV infection caused the mol% of a larger number of free amino acids to rise in B. tabaci Q than in B, and the analysis of honeydew indicated that, when feeding on TYLCV-infected plants, B. tabaci Q was better able to use the free amino acids than B. tabaci B. The results suggest that B. tabaci Q is better adapted than B to feed on TYLCV-infected plants, and that TYLCV alters the B. tabaci B–Q competitive interaction in favor of Q