Training on Thin Air: Improve Image Classification with Generated Data

Acquiring high-quality data for training discriminative models is a crucial yet challenging aspect of building effective predictive systems. In this paper, we present Diffusion Inversion, a simple yet effective method that leverages the pre-trained generative model, Stable Diffusion, to generate diverse, high-quality training data for image classification. Our approach captures the original data distribution and ensures data coverage by inverting images to the latent space of Stable Diffusion, and generates diverse novel training images by conditioning the generative model on noisy versions of these vectors. We identify three key components that allow our generated images to successfully supplant the original dataset, leading to a 2-3x enhancement in sample complexity and a 6.5x decrease in sampling time. Moreover, our approach consistently outperforms generic prompt-based steering methods and KNN retrieval baseline across a wide range of datasets. Additionally, we demonstrate the compatibility of our approach with widely-used data augmentation techniques, as well as the reliability of the generated data in supporting various neural architectures and enhancing few-shot learning

Ultrasensitive N-photon interferometric autocorrelator

We demonstrate a novel method to measure the Nth-order (N=1, 2, 3, 4) interferometric autocorrelation with high sensitivity and temporal resolution. It is based on the combination of linear absorption and nonlinear detection in a superconducting nanodetector, providing much higher efficiency than methods based on all-optical nonlinearities. Its temporal resolution is only limited by the quasi-particle energy relaxation time, which is directly measured to be in the 20 ps range for the NbN films used in this work. We present a general model of interferometric autocorrelation with these nonlinear detectors and discuss the comparison with other approaches and possible improvements

Improving Malware Detection By Parsing Broken Code

Spreadsheets, word processors, and other document editing applications enable users to write scripts or macros that automate a sequence of actions, e.g., keystrokes, mouse-clicks, etc. through code. Although macros can improve user efficiency by automating repetitive actions, executable code within a document can also potentially include malware. Macro-based malware is known to intentionally use broken syntax to bypass detection. This disclosure describes a parser that is resilient to syntax errors in code, and which can, by applying local corrections, continue to parse the rest of the code after encountering a parse error. Once corrected, the code can be subject to malware detection prior to or after translation into the target language

Spatio-Temporal Branching for Motion Prediction using Motion Increments

Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy

Large Language Models Are Human-Level Prompt Engineers

By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the "program," optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 19/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer

A keystone microbial enzyme for nitrogen control of soil carbon storage

This is the final version. Available on open access from AAAS via the DOI in this recordData and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials and figshare (https://figshare.com/s/37aa98b76a7ef51da2e2). Correspondence should be addressed to Y.L. ([email protected]). Requests for additional materials and database should be addressed to J. Cao ([email protected]), R.-w.W. ([email protected]), and X.Z. ([email protected]).Agricultural and industrial activities have increased atmospheric nitrogen (N) deposition to ecosystems worldwide. N deposition can stimulate plant growth and soil carbon (C) input, enhancing soil C storage. Changes in microbial decomposition could also influence soil C storage, yet this influence has been difficult to discern, partly because of the variable effects of added N on the microbial enzymes involved. We show, using meta-analysis, that added N reduced the activity of lignin-modifying enzymes (LMEs), and that this N-induced enzyme suppression was associated with increases in soil C. In contrast, N-induced changes in cellulase activity were unrelated to changes in soil C. Moreover, the effects of added soil N on LME activity accounted for more of the variation in responses of soil C than a wide range of other environmental and experimental factors. Our results suggest that, through responses of a single enzyme system to added N, soil microorganisms drive long-term changes in soil C accumulation. Incorporating this microbial influence on ecosystem biogeochemistry into Earth system models could improve predictions of ecosystem C dynamics.Fundamental Research Funds for the Central Universitiesational Natural Science Foundation of China (NSFC)China Postdoctoral Science FoundationNatural Science Basic Research Plan in Shaanxi ProvinceState Key Laboratory of Loess and Quaternary GeologyKey Laboratory of Aerosol Chemistry and PhysicsInstitute of Earth Environment, Chinese Academy of SciencesUS Department of EnergyNSFNSFC-Yunnan United FundNational Science Fund for Distinguished Young ScholarsChina Scholarship Counci

ANALYSIS OF JIAYUGUAN [嘉峪关] PAVILIONS' DEFORMATION AND ITS INFLUENCE FACTORS WITH THE APPLICATION OF COMPREHENSIVE TECHNOLOGY

In the case study of Guanghua Pavilion [光化楼] located in Jiayuguan [嘉峪关] City, Gansu province in China, the 3D laser scanning technology and leveling technology are used to analyze overall tilt, columns tilt and uneven settlement of the pavilion. It is found that Guanghua Pavilion [光化楼] tilted to the southeast; most of the columns from the first floor to the third floor tilted to the northeast and east directions, and all the tilted distance of the columns is within 100 mm.; a certain amount of uneven settlement happened on Guanghua Pavilion [光化楼], in which the east side sinks more obviously. In the process of analyzing the influencing factors of deformation, numerical wind tunnel simulation and Midas Gen modeling methods are used. It is concluded that the external wind load is the main cause of Jiayuguan [嘉峪关] pavilions’ deformation

A topological Dirac insulator in a quantum spin Hall phase : Experimental observation of first strong topological insulator

When electrons are subject to a large external magnetic field, the conventional charge quantum Hall effect \cite{Klitzing,Tsui} dictates that an electronic excitation gap is generated in the sample bulk, but metallic conduction is permitted at the boundary. Recent theoretical models suggest that certain bulk insulators with large spin-orbit interactions may also naturally support conducting topological boundary states in the extreme quantum limit, which opens up the possibility for studying unusual quantum Hall-like phenomena in zero external magnetic field. Bulk Bi$_{1-x}$Sb$_x$ single crystals are expected to be prime candidates for one such unusual Hall phase of matter known as the topological insulator. The hallmark of a topological insulator is the existence of metallic surface states that are higher dimensional analogues of the edge states that characterize a spin Hall insulator. In addition to its interesting boundary states, the bulk of Bi$_{1-x}$Sb$_x$ is predicted to exhibit three-dimensional Dirac particles, another topic of heightened current interest. Here, using incident-photon-energy-modulated (IPEM-ARPES), we report the first direct observation of massive Dirac particles in the bulk of Bi$_{0.9}$Sb$_{0.1}$, locate the Kramers' points at the sample's boundary and provide a comprehensive mapping of the topological Dirac insulator's gapless surface modes. These findings taken together suggest that the observed surface state on the boundary of the bulk insulator is a realization of the much sought exotic "topological metal". They also suggest that this material has potential application in developing next-generation quantum computing devices.Comment: 16 pages, 3 Figures. Submitted to NATURE on 25th November(2007