Responding to Agency Avoidance of OIRA

Concerns have recently been raised that US federal agencies may sometimes avoid regulatory review by the White House Office of Information and Regulatory Affairs (OIRA). In this article, we assess the seriousness of such potential avoidance, and we recommend a framework for evaluating potential responses. After summarizing the system of presidential regulatory oversight through OIRA review, we analyze the incentives for agencies to cooperate with or avoid OIRA. We identify a wider array of agency avoidance tactics than has past scholarship, and a wider array of corresponding response options available to OIRA, the President, Congress, and the courts. We argue that, because the relationship between agencies and OIRA involves ongoing repeat player interactions, some of these avoidance tactics are less likely to occur (or to succeed) than has previously been alleged, and others are more likely; the difference depends significantly on how easy it is for OIRA to detect avoidance, and for OIRA, the courts, and others to respond. Further, we note that in this repeat player relationship, responses to agency avoidance tactics may induce further strategic moves and countermoves. Thus we further argue that the optimal response may not always be to try to eliminate the avoidance behavior; some avoidance may be worth tolerating where the benefits of trying to reduce agency avoidance would not justify the costs of response options and countermoves. We therefore conclude that responses to agency avoidance should be evaluated in a way similar to what OIRA asks of agencies evaluating proposed regulations: by weighing the pros and cons of alternative response options (including no action)

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using CT Images and Text

Prosthetic Joint Infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based feature fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4\% and an area under the curve (AUC) of 95.9\%, outperforming recent multimodal approaches by 2.9\% in ACC and 2.2\% in AUC, with a parameter count of only 68M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice

Coming into the Anthropocene

This essay reviews Professor Jonathan Cannon’s Environment in the Balance. Cannon’s book admirably analyzes the Supreme Court’s uptake of, or refusal of, the key commitments of the environmental-law revolution of the early 1970s. In some areas the Court has adapted old doctrines, such as Standing and Commerce, to accommodate ecological insights; in other areas, such as Property, it has used older doctrines to restrain the transformative effects of environmental law. After surveying Cannon’s argument, this review diagnoses the historical moment that has made the ideological division that Cannon surveys especially salient: a time of stalled legislation, political deadlock, and highly contested regulatory and judicial interpretation. This analysis, however, does not limit the interest of Cannon’s analysis to this political moment. Rather, Cannon’s integration of legal and cultural analysis has great promise for the Anthropocene, the dawning era when human decisions and values will be among the most important forces shaping the planet. In the future, it will be necessary to think of environmental law as both reflecting and producing ideas of the value and meaning of the natural world. Cannon’s analysis is an excellent starting point for an Anthropocene approach

Fosilni les Carapoxylon iz spodnjemiocenskih plasti pri Komendi

Pieces of fossil wood were discovered near Komenda from the Lower Miocene beds. According to microscopic wood anatomy it corresponds to the family Meliaceae and to the genus Carapoxylon. This is the first identification of Miocene hardwood from Slovenia. Fossil woods of genus Carapoxylon are most common in the Ottnangian to Badenian period in central Europe (North Alpine Molasse Basin). The nearest living relatives of Carapoxylon belong to genus Carapa, Xylocarpus and Entandrophragma

Orbital and spin magnetic moments of transforming 1D iron inside metallic and semiconducting carbon nanotubes

The orbital and spin magnetic properties of iron inside transforming metallic and semiconducting 1D carbon nanotube hybrids are studied by means of local x-ray magnetic circular dichroism (XMCD) and bulk superconducting quantum interference device (SQUID) measurements. Nanotube hybrids are initially ferrocene filled single-walled carbon nanotubes (SWCNT) of different metallicities. After a high temperature nanochemical reaction ferrocene molecules react with each other to form iron nano clusters. We show that the ferrocenes molecular orbitals interact differently with the SWCNT of different metallicities without significant XMCD response. This XMCD at various temperatures and magnetic fields reveals that the orbital and/or spin magnetic moments of the encapsulated iron are altered drastically as the transformation to 1D Fe nanoclusters takes place. The orbital and spin magnetic moments are both found to be larger in filled semiconducting nanotubes than in the metallic sample. This could mean that the magnetic polarizations of the encapsulated material is dependent on the metallicity of the tubes. From a comparison between the iron 3d magnetic moments and the bulk magnetism measured by SQUID, we conclude that the delocalized magnetisms dictate the magnetic properties of these 1D hybrid nanostructures

Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts

Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different “detectability” patterns caused by deformations, occlusion and/or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1% AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part.This material is based upon work supported by the Center for Minds, Brains and Machines (CBMM), funded by NSF STC award CCF-1231216

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

A SVM-based method for identifying fracture modes of rock using WVD spectrogram features of AE signals

In order to achieve the highly efficient and accurate identification of fracture modes including tension or shear fractures during rock failure, an intelligent identification method based on Wigner-Ville distribution (WVD) spectrogram features of acoustic emission (AE) signals was proposed. This method was mainly constructed by the following steps: Firstly, AE hits corre-sponding to tension and shear fractures were obtained through conducting the Brazilian disc test (tension fracture) and direct shear test (shear fracture) of limestone. Secondly, the WVD spectro-grams of these tensile-type and shear-type AE hits were respectively extracted and then trans-formed into the image features of relatively low-dimension as the sample set based on the gray-level cooccurrence matrix (GLCM) and histogram of oriented gradient (HOG). Finally, on the basis of the processed and classified sample set of the WVD spectrogram features, an identifica-tion model of rock fracture modes was established by a support vector machine (SVM) learning algorithm. To verify this method, the fracture modes of limestone subjected to biaxial compres-sion were identified by the method. The results showed that the method not only can greatly re-veal the fracture modes change from tension-dominated to shear-dominated fractures, but also has advantages over the RA-AF value method, such as applicability, accuracy and practicality