EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Detecting anomalies in images is an important task, especially in real-time computer vision applications. In this work, we focus on computational efficiency and propose a lightweight feature extractor that processes an image in less than a millisecond on a modern GPU. We then use a student-teacher approach to detect anomalous features. We train a student network to predict the extracted features of normal, i.e., anomaly-free training images. The detection of anomalies at test time is enabled by the student failing to predict their features. We propose a training loss that hinders the student from imitating the teacher feature extractor beyond the normal images. It allows us to drastically reduce the computational cost of the student-teacher model, while improving the detection of anomalous features. We furthermore address the detection of challenging logical anomalies that involve invalid combinations of normal local features, for example, a wrong ordering of objects. We detect these anomalies by efficiently incorporating an autoencoder that analyzes images globally. We evaluate our method, called EfficientAD, on 32 datasets from three industrial anomaly detection dataset collections. EfficientAD sets new standards for both the detection and the localization of anomalies. At a latency of two milliseconds and a throughput of six hundred images per second, it enables a fast handling of anomalies. Together with its low error rate, this makes it an economical solution for real-world applications and a fruitful basis for future research

Electromagnetic transition form factors and dilepton decay rates of nucleon resonances

Relativistic, kinematically complete phenomenological expressions for the dilepton decay rates of nucleon resonances with arbitrary spin and parity are derived in terms of the magnetic, electric, and Coulomb transition form factors. The dilepton decay rates of the nucleon resonances with masses below 2 GeV are estimated using the extended vector meson dominance model for the transition form factors. The model provides a unified description of the photo- and electroproduction data, the vector meson decays, and the dilepton decays of the nucleon resonances. The constraints on the transition form factors from the quark counting rules are taken into account. The parameters of the model are fixed by fitting the available photo- and electroproduction data and using results of the multichannel partial-wave analysis of the $\pi N$ scattering. Where experimental data are not available, predictions of the non-relativistic quark models are used as an input. The vector meson coupling constants of the magnetic, electric, and Coulomb types are determined. The dilepton widths and the dilepton spectra from decays of nucleon resonances with masses below 2 GeV are calculated.Comment: An error in the code is found and fixed. Numerical results for the spin-half nucleon resonances changed. A few misprints are removed from the text. 56 pages including 7 tables and 27 eps figures, REVTe

GermanPartiesQA: Benchmarking Commercial Large Language Models for Political Bias and Sycophancy

LLMs are changing the way humans create and interact with content, potentially affecting citizens' political opinions and voting decisions. As LLMs increasingly shape our digital information ecosystems, auditing to evaluate biases, sycophancy, or steerability has emerged as an active field of research. In this paper, we evaluate and compare the alignment of six LLMs by OpenAI, Anthropic, and Cohere with German party positions and evaluate sycophancy based on a prompt experiment. We contribute to evaluating political bias and sycophancy in multi-party systems across major commercial LLMs. First, we develop the benchmark dataset GermanPartiesQA based on the Voting Advice Application Wahl-o-Mat covering 10 state and 1 national elections between 2021 and 2023. In our study, we find a left-green tendency across all examined LLMs. We then conduct our prompt experiment for which we use the benchmark and sociodemographic data of leading German parliamentarians to evaluate changes in LLMs responses. To differentiate between sycophancy and steerabilty, we use 'I am [politician X], ...' and 'You are [politician X], ...' prompts. Against our expectations, we do not observe notable differences between prompting 'I am' and 'You are'. While our findings underscore that LLM responses can be ideologically steered with political personas, they suggest that observed changes in LLM outputs could be better described as personalization to the given context rather than sycophancy.Comment: 12 page

The P_33(1232) resonance contribution into the amplitudes M_{1+}^{3/2},E_{1+}^{3/2},S_{1+}^{3/2} from an analysis of the p(e,e'p)\pi^0 data at Q^2 = 2.8, 3.2, and 4 (GeV/c)^2 within dispersion relation approach

Within the fixed-t dispersion relation approach we have analysed the TJNAF and DESY data on the exclusive p(e,e'p)\pi^0 reaction in order to find the P_{33}(1232) resonance contribution into the multipole amplitudes M_{1+}^{3/2},E_{1+}^{3/2},S_{1+}^{3/2}. As an input for the resonance and nonresonance contributions into these amplitudes the earlier obtained solutions of the integral equations which follow from dispersion relations are used. The obtained values of the ratio E2/M1 for the \gamma^* N \to P_{33}(1232) transition are: 0.039\pm 0.029, 0.121\pm 0.032, 0.04\pm 0.031 for Q^2= 2.8, 3.2, and 4 (GeV/c)^2, respectively. The comparison with the data at low Q^2 shows that there is no evidence for the presence of the visible pQCD contribution into the transition \gamma N \to P_{33}(1232) at Q^2=3-4 GeV^2. The ratio S_{1+}^{3/2}/M_{1+}^{3/2} for the resonance parts of multipoles is: -0.049\pm 0.029, -0.099\pm 0.041, -0.085\pm 0.021 for Q^2= 2.8, 3.2, and 4 (GeV/c)^2, respectively. Our results for the transverse form factor G_T(Q^2) of the \gamma^* N \to P_{33}(1232) transition are lower than the values obtained from the inclusive data. With increasing Q^2, Q^4G_T(Q^2) decreases, so there is no evidence for the presence of the pQCD contribution here too

Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.Comment: main text: 21 pages, 9 figures, 2 tables. supplementary information: 57 pages, 83 figures, 20 table

Generative Hierarchical Materials Search

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future.https://generative-materials.github.io

METODOLOGIA ALTERNATIVA NO PROCESSO DE ENSINO APRENDIZAGEM SOBRE EMBRIOLOGIA E REPRODUÇÃO HUMANA PARA ALUNOS DO ENSINO MÉDIO DAS ESCOLAS DE BLUMENAU

A inovação das metodologias de ensino durante o período escolar é de extrema importância para melhoria do conhecimento e do interesse por parte dos alunos acerca dos assuntos de embriologia e reprodução humana. No presente estudo foram coletadas informações referentes a idade e sexo de cada aluno participante da palestra. Também foi aplicado um questionário composto por perguntas acerca dos assuntos Embriologia e Reprodução Humana, previamente à palestra e após a mesma. Nas palestras foram utilizados materiais didáticos – maquetes, vídeos, fetos para exposição –, juntamente com a explicação por parte das bolsistas e voluntárias do projeto de extensão, a respeito dos assuntos já citados. Com a utilização de um método expositivo alternativo, comprovou-se uma melhoria no nível de informação sobre os temas Embriologia e Reprodução Humana por parte dos escolares avaliados, o que sugere uma mudança comportamental devido às palestras educativas

METODOLOGIA ALTERNATIVA NO PROCESSO DE ENSINO APRENDIZAGEM SOBRE EMBRIOLOGIA E REPRODUÇÃO HUMANA PARA ALUNOS DO ENSINO MÉDIO DAS ESCOLAS DE BLUMENAU

A inovação das metodologias de ensino durante o período escolar é de extrema importância para melhoria do conhecimento e do interesse por parte dos alunos acerca dos assuntos de embriologia e reprodução humana. No presente estudo foram coletadas informações referentes a idade e sexo de cada aluno participante da palestra. Também foi aplicado um questionário composto por perguntas acerca dos assuntos Embriologia e Reprodução Humana, previamente à palestra e após a mesma. Nas palestras foram utilizados materiais didáticos – maquetes, vídeos, fetos para exposição –, juntamente com a explicação por parte das bolsistas e voluntárias do projeto de extensão, a respeito dos assuntos já citados. Com a utilização de um método expositivo alternativo, comprovou-se uma melhoria no nível de informação sobre os temas Embriologia e Reprodução Humana por parte dos escolares avaliados, o que sugere uma mudança comportamental devido às palestras educativas