Large Language Models for Automated Open-domain Scientific Hypotheses Discovery

Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past research on hypothetical induction has a limited setting that (1) the observation annotations of the dataset are not raw web corpus but are manually selected sentences (resulting in a close-domain setting); and (2) the ground truth hypotheses annotations are mostly commonsense knowledge, making the task less challenging. In this work, we propose the first NLP dataset for social science academic hypotheses discovery, consisting of 50 recent papers published in top social science journals. Raw web corpora that are necessary for developing hypotheses in the published papers are also collected in the dataset, with the final goal of creating a system that automatically generates valid, novel, and helpful (to human researchers) hypotheses, given only a pile of raw web corpora. The new dataset can tackle the previous problems because it requires to (1) use raw web corpora as observations; and (2) propose hypotheses even new to humanity. A multi-module framework is developed for the task, as well as three different feedback mechanisms that empirically show performance gain over the base framework. Finally, our framework exhibits high performance in terms of both GPT-4 based evaluation and social science expert evaluation

Translithospheric magma plumbing system of intraplate volcanoes as revealed by electrical resistivity imaging

AbstractThe magma plumbing systems of volcanoes in subduction and divergent tectonic settings are relatively well known, whereas those of intraplate volcanoes remain elusive; robust geophysical information on the magma pathways and storage zones is lacking. We inverted magnetotelluric data to image the magma plumbing system of an intraplate monogenetic volcanic field located above the stagnant Pacific slab in northeast China. We identified a complex, vertically aligned, low-resistivity anomaly system extending from the asthenosphere to the surface consisting of reservoirs with finger- to lens-like geometries. We show that magma forms as CO2-rich melts in a 150-km-deep asthenospheric plume crossing the whole lithosphere as hydrated melt, inducing underplating at 50 km depth, evolving in crustal reservoirs, and erupting along dikes. Intraplate volcanoes are characterized by low degrees of melting and low magma supply rates. Their plumbing systems have a geometry not so different from that of volcanoes in subduction settings

A large ectopic hepatocellular carcinoma with adrenal infiltration: a rare case report

Ectopic hepatocellular carcinoma (EHCC) originates from the ectopic liver, which refers to a liver organ or tissue unrelated to surrounding tissues. EHCC is a rare disease that lacks specific clinical signs, and preoperative diagnosis is often difficult. In a 61-year-old male patient with positive hepatitis B virus antibody, abdominal contrast-enhanced computed tomography scan showed a large heterogenously enhancing mass both on arterial and portal venous phase imaging arising from the right adrenal gland. Similar enhancement features were seen on magnetic resonance imaging. Serum potassium, aldosterone, cortisol, and plasma metanephrines were normal. The tumor markers of serum alpha-fetoprotein and alpha-fetoprotein-L3% were increased to 23.69 ng/mL and 82.1%, respectively. Exploratory laparotomy was performed and operative findings showed that the retroperitoneal tumor was disconnected from the right kidney and the liver, but invaded the right adrenal gland. Immunohistochemical examination showed that Arginase-1 was positive expression, and the retroperitoneal tumor was finally diagnosed as EHCC. We report a rare EHCC with adrenal infiltration that is difficult to diagnose preoperatively and mimics a retroperitoneal tumor or adrenal tumor, and we present a review of the literature on EHCC case reports

Automated Driving Systems Data Acquisition and Processing Platform

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the sensor data from multiple CAVs and extract the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multiple CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, a post-processing method is proposed to refine trajectories from the object tracking algorithms. Aiming to tackle the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories of the same object. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the post-processing algorithm's improvements in noise level and outlier removal are discussed, confirming the functionality and effectiveness of the proposed holistic data collection and processing platform

Upregulation of the ZWINT expression correlates with prostate cancer progression and immune infiltration

Prostate cancer (PCa), the most prevalent epithelial malignant neoplasm in the male group globally, is the fifth largest cause of cancer-related death in males. ZW10 Interactor (ZWINT) is involved in the chromosome segregation process, which is linked to the formation of several tumor cells. However, its function in PCa remains unknown. Therefore, our aim was to explore the potential mechanisms of ZWINT in PCa progression. We obtained RNA-seq as well as clinical data from The Cancer Genome Atlas Program (TCGA), University of California Santa Cruz (UCSC) database. Assessment of ZWINT expression in clinical subgroups, immune infiltration, and prognostic relevance using the R program. Search Tool for Recurring Instances of Neighbouring Genes (STRING) tool was applied to construct a ZWINT co-expression network and the potential biological functions involved in differentially expressed genes (DEGs) were investigated by enrichment analysis. ZWINT was upregulated in prostate cancer tissues and showed to be significantly associated with T stage, N stages, Gleason score (GS), and prognosis of prostate cancer patients. Functional enrichment analysis revealed that ZWINT-related genes were mainly related to cell cycle, meiosis, myogenic fiber synthesis, and muscle contraction. In addition, High-expression of ZWINT may have possessed immunosuppressive effects through adverse regulation of several immune cells and factors. ZWINT is overexpressed in prostate cancer and correlated with immune infiltration, which is expected to be a potential biomarker for PCa prognosis

ResMem: Learn what you can and memorize the rest

The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMem) algorithm, a new method that augments an existing prediction model (e.g. a neural network) by fitting the model's residuals with a $k$-nearest neighbor based regressor. The final prediction is then the sum of the original model and the fitted residual regressor. By construction, ResMem can explicitly memorize the training labels. Empirically, we show that ResMem consistently improves the test set generalization of the original prediction model across various standard vision and natural language processing benchmarks. Theoretically, we formulate a stylized linear regression problem and rigorously show that ResMem results in a more favorable test risk over the base predictor

Gate-Compatible Circuit QED in a Three-Dimensional Cavity Architecture

Semiconductor-based superconducting qubits offer a versatile platform for studying hybrid quantum devices in circuit quantum electrodynamics (cQED) architecture. Most of these cQED experiments utilize coplanar waveguides, where the incorporation of DC gate lines is straightforward. Here, we present a technique for probing gate-tunable hybrid devices using a three-dimensional (3D) microwave cavity. A recess is machined inside the cavity wall for the placement of devices and gate lines. We validate this design using a hybrid device based on an InAs-Al nanowire Josephson junction. The coupling between the device and the cavity is facilitated by a long superconducting strip, the antenna. The Josephson junction and the antenna together form a gatemon qubit. We further demonstrate the gate-tunable cavity shift and two-tone qubit spectroscopy. This technique could be used to probe various quantum devices and materials in a 3D cQED architecture that requires DC gate voltages

Dynamic Responses of Continuous Girder Bridges with Uniform Cross-Section under Moving Vehicular Loads

To address the drawback of traditional method of investigating dynamic responses of the continuous girder bridge with uniform cross-section under moving vehicular loads, the orthogonal experimental design method is proposed in this paper. Firstly, some empirical formulas of natural frequencies are obtained by theoretical derivation and numerical simulation. The effects of different parameters on dynamic responses of the vehicle-bridge coupled vibration system are discussed using our own program. Finally, the orthogonal experimental design method is proposed for the dynamic responses analysis. The results show that the effects of factors on dynamic responses are dependent on both the selected position and the type of the responses. In addition, the interaction effects between different factors cannot be ignored. To efficiently reduce experimental runs, the conventional orthogonal design is divided into two phases. It has been proved that the proposed method of the orthogonal experimental design greatly reduces calculation cost, and it is efficient and rational enough to study multifactor problems. Furthermore, it provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the codes of design and evaluation