Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissolution degree distribution law with depth in building ground in karst terrain

Based on dissolution mechanics of carbonate rock, the variation of dissolution capacity and degree with depth in building foundations were analyzed from the viewpoints of rocks' solubility and the water's dissolution capability, and they were both verified with specific engineering cases. The results show that the curve fitting coefficients of dissolution rate curve fitted by a depth-exponential function are generally more than 0.90, which indicates that dissolution degree in karst ground dominated by carbonate rocks generally decreases exponentially with the increase of depth. The rock fissures are the key factor for distribution feature of dissolution degree, and rock type and dissolution capacity of groundwater are important for regional karst feature distinct but not obvious for dissolution degree variation with depth in karst building ground. Dissolution degree of rock surface is higher than cavern dissolution degree, and so a reasonable dissolution degree indicator should not overlook the dissolution characteristics of rock surface, otherwise the depth attenuation law of dissolution degree will not be reflected rationally.
The Importance of Anti-Aliasing in Tiny Object Detection

Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. However, convolutional neural networks (CNNs) used as the backbone for object detection architectures typically neglect Nyquist's sampling theorem during down-sampling operations, resulting in aliasing and degraded performance. This is likely to be a particular issue for tiny objects that occupy very few pixels and therefore have high spatial frequency features. This paper applied an existing approach WaveCNet for anti-aliasing to tiny object detection. WaveCNet addresses aliasing by replacing standard down-sampling processes in CNNs with Wavelet Pooling (WaveletPool) layers, effectively suppressing aliasing. We modify the original WaveCNet to apply WaveletPool in a consistent way in both pathways of the residual blocks in ResNets. Additionally, we also propose a bottom-heavy version of the backbone, which further improves the performance of tiny object detection while also reducing the required number of parameters by almost half. Experimental results on the TinyPerson, WiderFace, and DOTA datasets demonstrate the importance of anti-aliasing in tiny object detection and the effectiveness of the proposed method which achieves new state-of-the-art results on all three datasets. Codes and experiment results are released at https://github.com/freshn/Anti-aliasing-Tiny-Object-Detection.git.
A Minkowski Functional Analysis of the Cosmic Microwave Background Weak Lensing Convergence

Minkowski functionals are summary statistics that capture the geometric and morphological properties of fields. They are sensitive to all higher order correlations of the fields and can be used to complement more conventional statistics, such as the power spectrum of the field. We develop a Minkowski functional-based approach for a full likelihood analysis of mildly non-Gaussian sky maps with partial sky coverage. Applying this to the inference of cosmological parameters from the Planck mission's map of the Cosmic Microwave Background's lensing convergence, we find an excellent agreement with results from the power spectrum-based lensing likelihood. While the non-Gaussianity of current CMB lensing maps is dominated by reconstruction noise, a Minkowski functional-based analysis may be able to extract cosmological information from the non-Gaussianity of future lensing maps and thus go beyond what is accessible with a power spectrum-based analysis. We make the numerical code for the calculation of a map's Minkowski functionals, skewness and kurtosis parameters available for download from GitHub.
Learning Interatomic Potentials at Multiple Scales

The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a multiple-time-step (MTS) integrator that evaluates certain potential energy terms that vary more slowly than others less frequently. This approach is enabled by the simple but limiting analytic forms of classical potentials. Machine learning interatomic potentials (MLIPs), in particular recent equivariant neural networks, are much more broadly applicable than classical potentials and can faithfully reproduce the expensive but accurate reference electronic structure calculations used to train them. They still, however, require the use of a single short time step, as they lack the inherent term-by-term scale separation of classical potentials. This work introduces a method to learn a scale separation in complex interatomic interactions by co-training two MLIPs. Initially, a small and efficient model is trained to reproduce short-time-scale interactions. Subsequently, a large and expressive model is trained jointly to capture the remaining interactions not captured by the small model. When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation. Compared to a conventionally trained MLIP, our approach can achieve a significant speedup (~3x in our experiments) without a loss of accuracy on the potential energy or simulation-derived quantities.
Quantum Electrodynamics with Time-varying Dielectrics

We present a framework for quantization of electromagnetic field in the presence of dielectric media with time-varying optical properties. Considering a microscopic model for the dielectric as a collection of matter fields interacting with the electromagnetic environment, we allow for the possibility of dynamically varying light-matter coupling. We obtain the normal modes of the coupled light-matter degrees of freedom, showing that the corresponding creation and annihilation operators obey equal-time canonical commutation relations. We show that these normal modes can consequently couple to quantum emitters in the vicinity of dynamic dielectric media, and the resulting radiative properties of atoms are thus obtained. Our results are pertinent to time-varying boundary conditions realizable across a wide range of state-of-the-art physical platforms and timescales.
Energetics of a Solar Flare and a Coronal Mass Ejection Generated by a Hot Channel Eruption

Hot channels (HCs) are prevalent in the solar corona and play a critical role in driving flares and coronal mass ejections (CMEs). In this paper, we estimate the energy content of an X1.4 eruptive flare with a fast CME generated by an HC eruption on 2011 September 22. Originating from NOAA Active Region 11302, the HC is the most dramatic feature in 131 and 94 Å images observed by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). The flare is simultaneously observed by SDO/AIA, the Reuven Ramaty High-energy Solar Spectroscopic Imager, and the Extreme-ultraviolet Imager on board the “behind” Solar Terrestrial Relations Observatory (STEREO). The CME is simultaneously detected by the white-light coronagraphs of the Large Angle Spectroscopic Coronagraph on board the Solar and Heliospheric Observatory and the COR1 coronagraph on board the behind STEREO. Using multiwavelength and multiview observations of the eruption, various energy components of the HC, flare, and CME are calculated. The thermal and kinetic energies of the HC are (1.77 ± 0.61) × 1030 erg and (2.90 ± 0.79) × 1030 erg, respectively. The peak thermal energy of the flare and total radiative loss of the soft X-ray–emitting plasma are (1.63 ± 0.04) × 1031 erg and (1.03–1.31) × 1031 erg, respectively. The ratio between the thermal energies of the HC and flare is 0.11 ± 0.03, suggesting that the thermal energy of the HC is not negligible. The kinetic and potential energies of the CME are (3.43 ± 0.94) × 1031 erg and (2.66 ± 0.49) × 1030 erg, yielding a total energy of (3.69 ± 0.98) × 1031 erg for the CME. Continuous heating of the HC is required to balance the rapid cooling by heat conduction, which probably originates from intermittent magnetic reconnection at the flare current sheet. Our investigation may provide insight into the buildup, release, and conversion of energies in large-scale solar eruptions.
Employing Feature Selection Algorithms to Determine the Immune State of a Mouse Model of Rheumatoid Arthritis.

The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance and population of inflammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the effectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is difficult if not impossible to determine prior to application of the therapy. To identify such states we consider a mouse model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for CIA; and use feature selection algorithms in order to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the efficacy of immunotherapy treatment.
Dissecting IoT Device Provisioning Process

We examine in detail the provisioning process used by many common, consumer-grade Internet of Things (IoT) devices. We find that this provisioning process involves the IoT device, the vendor's cloud-based server, and a vendor-provided mobile app. In order to better understand this process, we develop two toolkits. IoT-Dissect I enables us to decrypt and examine the messages exchanged between the IoT device and the vendor's server, and between the vendor's server and a vendor-provided mobile app. IoT-Dissect II permits us to reverse engineer the vendor's mobile app and observe its operation in detail. We find several potential security issues with the provisioning process and recommend ways to mitigate these potential problems. Further, based on these observations, we conclude that it is likely feasible to construct a vendor-agnostic IoT home gateway that will automate this largely manual provisioning process, isolate IoT devices on their own network, and perhaps open the tight association between an IoT device and the vendor's server.
Topologically Variable and Volumetric Morphing of 3D Architected Materials with Shape Locking

The morphing of 3D structures is suitable for i) future tunable material design for customizing material properties and ii) advanced manufacturing tools for fabricating 3D structures on a 2D plane. However, there is no inverse design method for topologically variable and volumetric morphing or morphing with shape locking, which limits practical engineering applications. In this study, we construct a general inverse design method for 3D architected materials for topologically variable and volumetric morphing, whose shapes are lockable in the morphed states, which can contribute to future tunable materials, design, and advanced manufacturing. Volumetric mapping of bistable unit cells onto any 3D morphing target geometry with kinematic and kinetic modifications can produce flat-foldable and volumetric morphing structures with shape-locking. This study presents a generalized inverse design method for 3D metamaterial morphing that can be used for structural applications with shape locking. Topologically variable morphing enables the manufacture of volumetric structures on a 2D plane, saving tremendous energy and materials compared with conventional 3D printing. Volumetric morphing can significantly expand the design space with tunable physical properties without limiting the selection of base materials.
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation

Human evaluation is increasingly critical for assessing large language models, capturing linguistic nuances, and reflecting user preferences more accurately than traditional automated metrics. However, the resource-intensive nature of this type of annotation process poses significant challenges. The key question driving our work:"is it feasible to minimize human-in-the-loop feedback by prioritizing data instances which most effectively distinguish between models? "We evaluate several metric-based methods and find that these metrics enhance the efficiency of human evaluations by minimizing the number of required annotations, thus saving time and cost, while ensuring a robust performance evaluation. We show that our method is effective across widely used model families, reducing instances of indecisive (or"tie") outcomes by up to 54% compared to a random sample when focusing on the top-20 percentile of prioritized instances. This potential reduction in required human effort positions our approach as a valuable strategy in future large language model evaluations.
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings

Stance detection is important for understanding different attitudes and beliefs on the Internet. However, given that a passage's stance toward a given topic is often highly dependent on that topic, building a stance detection model that generalizes to unseen topics is difficult. In this work, we propose using contrastive learning as well as an unlabeled dataset of news articles that cover a variety of different topics to train topic-agnostic/TAG and topic-aware/TAW embeddings for use in downstream stance detection. Combining these embeddings in our full TATA model, we achieve state-of-the-art performance across several public stance detection datasets (0.771 $F_1$-score on the Zero-shot VAST dataset). We release our code and data at https://github.com/hanshanley/tata.
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery.
A Taxonomic Revision of Monopteryx (Leguminosae): A Florally Divergent and Ancient Papilionoid Genus of Large Amazonian Trees

Abstract Monopteryx is a florally divergent genus of Dipterygeae, an early-branching papilionoid legume clade largely marked by winged papilionate floral architecture, expanded upper calyx lobes often assuming a wing-shaped orientation, and petals differentiated into standard, wings, and a keel enclosing the basally connate stamens. In contrast to the remaining Dipterygeae genera, Monopteryx has differentiated petals but the marginally coherent keel with interlaced trichomes exposes the free stamens and the expanded upper calyx lobes are nearly entirely fused with a standard-like dorsal orientation. Monopteryx species are restricted to the Amazonian rainforests, where they have diversified since the last ∼15 Ma, but the divergence of the genus is estimated to be as old as ∼39 Ma. They grow as large buttressed trees usually with a uniquely “flying” architecture, which are arched from the trunk to the ground and separated from one another, unlike that found in any other species of leguminaceous trees. Its fruits are elastically dehiscent pods and in some species they bear marginally crimped wings along the sutures. Our taxonomic revision of this ecologically and evolutionarily important, ancient genus includes an analysis of about 135 specimens from across 14 herbaria, including both type and historical collections, as well as recently collected samples from our extensive fieldwork across remote areas of the Amazon. Grounded on a densely-sampled dated molecular phylogeny of nuclear and plastid data, here we recognize three phylogenetically and morphologically distinct taxa: M. angustifolia, M. inpae, and M. uaucu. After a careful revision of their nomenclatural history, we also found that M. inpae was not validly published. We subsequently have provided typification of all names associated with species of the genus. This revision also includes morphological descriptions, illustrations, and distribution maps for all species. We also discuss the phylogenetic relationships between the species and the evolution of selected taxonomically key morphological characters in the context of the entire Diptergyeae clade.
Outcomes of Endoscopic Biliary Drainage in Postsurgical Anatomy Using Endoscopic Ultrasound and Enteroscopy: A Comparative Study

Abstract Objectives Biliary obstruction is a common problem encountered in postsurgical anatomy, which may lead to serious complications if not treated promptly. Endoscopic drainage is a minimally invasive and effective treatment option for such patients. However, the optimal route of endoscopic drainage, either SBE-ERCP (single-balloon enteroscopy with endoscopic retrograde cholangiopancreatography) or EUS-BD (endoscopic ultrasound-guided bile duct drainage), remains controversial. In this study, we aim to evaluate the feasibility and outcomes of endoscopic drainage using these two techniques in postsurgical biliary obstruction over a period of 7 years. Materials and Methods We conducted a retrospective study of patients who underwent endoscopic drainage for postsurgical biliary obstruction using SBE-ERCP or EUS-BD techniques between 2015 and 2022. The demographic details, clinical presentation, procedure duration, number of sessions required, technical success, complications, and change of procedure from SBE-ERCP to EUS-BD or vice versa were recorded. Results Seventy-five patients, predominantly females with a mean age of 48 years, underwent endoscopic drainage. Forty-eight patients underwent SBE drainage and 27 patients underwent EUS-HG (EUS-guided hepaticogastrostomy). The mean duration of procedure (44 vs. 77 minutes), number of complications (4 vs. 5), technical success rate (93.5 vs. 85%), change of procedure (0 vs. 3), and number of sessions (1.1 vs. 1.8) were significantly less in the EUS-HG as compared to SBE-ERCP. Conclusions Endoscopic biliary drainage is feasible, safe, and effective in postsurgical biliary anatomy but requires high technical expertise. The study proposes an algorithm that can be applied in such group of patients to determine the route for choosing the drainage procedure. This requires further validation with a large prospective cohort.
Correct and Incorrect Recommendations for or against Fine Needle Biopsies of Hypofunctioning Thyroid Nodules: Performance of Different Ultrasound-based Risk Stratification Systems

Abstract Purpose To evaluate the recommendations for or against fine needle biopsy (FNB) of hypofunctioning thyroid nodules (TNs) using of five different Ultrasound (US) -based risk stratification systems (RSSs). Methods German multicenter study with 563 TNs (≥ 10 mm) in 534 patients who underwent thyroid US and surgery. All TNs were evaluated with ACR TI-RADS, EU-TIRADS, ATA, K-TIRADS 2016 and modified K-TIRADS 2021. A correct recommendation was defined as: malignant TN with recommendation for FNB (appropriate) or benign TN without recommendation for FNB (avoided). An incorrect recommendation was defined as: malignant TN without recommendation for FNB (missed) or benign TN with recommendation for FNB (unnecessary). Results ACR TI-RADS demonstrated the highest rate of correct (42.3 %) and lowest rate of incorrect recommendations (57.7 %). The other RRSs showed similar results for correct (26.5 %–35.7 %) and incorrect (64.3 %–73.5 %) recommendations. ACR TI-RADS demonstrated the lowest rate of unnecessary (73.4 %) and the highest rate of appropriate (26.6 %) FNB recommendation. For other RSSs, the rates of unnecessary and appropriate FNB were between 75.2 %–77.1 % and 22.9 %–24.8 %. The lowest rate of missed FNB (14.7 %) and the highest rate of avoided FNB (85.3 %) was found for ACR TI-RADS. For the other RSSs, the rates of missed and avoided FNB were between 17.8 %–26.9 % and 73.1 %–82.2 %. When the size cutoff was disregarded, an increase of correct recommendations and a decrease of incorrect recommendations was observed for all RSSs. Conclusion The RSSs vary in their ability to correctly recommend for or against FNB. An understanding of the impact of nodule size cutoffs seems necessary for the future of TIRADS.