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Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factual hallucination, which can lead to incorrect diagnoses. Fine-tuning and retrieval-augmented generation (RAG) have emerged as methods to address these issues. However, the amount of high-quality data and distribution shifts between training data and deployment data limit the application of fine-tuning methods. Although RAG is lightweight and effective, existing RAG-based approaches are not sufficiently general to different medical domains and can potentially cause misalignment issues, both between modalities and between the model and the ground truth. In this paper, we propose a versatile multimodal RAG system, MMed-RAG, designed to enhance the factuality of Med-LVLMs. Our approach introduces a domain-aware retrieval mechanism, an adaptive retrieved contexts selection, and a provable RAG-based preference fine-tuning strategy. These innovations make the RAG process sufficiently general and reliable, significantly improving alignment when introducing retrieved contexts. Experimental results across five medical datasets (involving radiology, ophthalmology, pathology) on medical VQA and report generation demonstrate that MMed-RAG can achieve an average improvement of 43.8% in factual accuracy in the factual accuracy of Med-LVLMs. 

Dynamic organization of the cytoskeletal filaments and rod-like proteins in the cell membrane and other biological interfaces occurs in many cellular processes. Previous modeling studies have considered the dynamics of a single rod on fluid planar membranes. We extend these studies to the more physiologically relevant case of a single filament moving in a spherical membrane. Specifically, we use a slender-body formulation to compute the translational and rotational resistance of a single filament of length L moving in a membrane of radius R and 2D viscosity ηm, and surrounded on its interior and exterior with Newtonian fluids of viscosities η− and η+. We first discuss the case where the filament's curvature is at its minimum κ=1/R. We show that the boundedness of spherical geometry gives rise to flow confinement effects that increase in strength with increasing the ratio of filament's length to membrane radius L/R. These confinement flows only result in a mild increase in filament's resistance along its axis, ξ∥, and its rotational resistance, ξΩ. As a result, our predictions of ξ∥ and ξΩ can be quantitatively mapped to the results on a planar membrane. In contrast, we find that the drag in perpendicular direction, ξ⊥, increases superlinearly with the filament's length, when L/R>1 and ultimately ξ⊥→∞ as L/R→π. Next, we consider the effect of the filament's curvature, κ, on its parallel motion, while fixing the membrane's radius. We show that the flow around the filament becomes increasingly more asymmetric with increasing its curvature. These flow asymmetries induce a net torque on the filament, coupling its parallel and rotational dynamics. This coupling becomes stronger with increasing L/R and κ. 

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. 

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours). We use multiple image representations to solve the problem. We use FM components to separate each group from background groups, AM-FM components for detecting the back-of-the-head, and YOLO for face detection. We use classroom videos from four different groups to validate our approach. Our use of multiple representations is shown to be significantly more accurate than the use of YOLO alone. 

Recent advances in the blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behavior of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for performance improvements, such as IOTA and Graphcoin. The first question that comes to mind is what improvements that a blockchain system would see by leveraging these new advances. In this paper, we consider three major properties for a blockchain system: α-partial verification, scalability, and finality-duration. We establish a formal framework and prove that no blockchain system can achieve ?-partial verification for any fixed constant ?, high scalability, and low finality-duration simultaneously. We observe that classical blockchain systems like Bitcoin achieves full verification (α=1) and low finality-duration, Ethereum 2.0 Sharding achieves low finality-duration and high scalability. We are interested in whether it is possible to partially satisfy the three properties. 

A search for proton decay into $e^{+}/{\mu }^{+}$and a $\eta$meson has been performed using data from a $0.373\mathrm{Mton}·\mathrm{year}$exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$interaction cross section, resulting in a factor of 2 reduction in uncertainties from this source and $\sim 10\%$increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times {10}^{34}\mathrm{years}$for $p\to e^{+}\eta$and $7.3\times {10}^{33}\mathrm{years}$for $p\to {\mu }^{+}\eta$at the 90% CL were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date. Published by the American Physical Society2024 

We present the results of the charge ratio ( $R$) and polarization ( $P_0^{\mu }$) measurements using decay electron events collected between September 2008 and June 2022 with the Super-Kamiokande detector. Because of its underground location and long operation, we are able to perform high-precision measurements by accumulating cosmic-ray muons. We measured the muon charge ratio to be $R=1.32\pm 0.02(\mathrm{stat}+\mathrm{syst})$at $E_{\mu }\cos {\theta }_{\text{Zenith}}={0.7}_{-0.2}^{+0.3}\mathrm{TeV}$, where $E_{\mu }$is the muon energy and ${\theta }_{\text{Zenith}}$is the zenith angle of incoming cosmic-ray muons. This result is consistent with the Honda flux model while indicating a tension with the $\pi K$model of $1.9\sigma$. We also measured the muon polarization at the production location to be $P_0^{\mu }=0.52\pm 0.02(\mathrm{stat}+\mathrm{syst})$at the muon momentum of ${0.9}_{-0.1}^{+0.6}\mathrm{TeV}/c$at the surface of the mountain; this also suggests a tension with the Honda flux model of $1.5\sigma$. This is the most precise measurement ever to experimentally determine the cosmic-ray muon polarization near $1\mathrm{TeV}/c$. These measurement results are useful to improve atmospheric neutrino simulations. Published by the American Physical Society2024 

Abstract Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data.