An Algebraic Method to Fidelity-based Model Checking over Quantum Markov Chains

Fidelity is one of the most widely used quantities in quantum information that measure the distance of quantum states through a noisy channel. In this paper, we introduce a quantum analogy of computation tree logic (CTL) called QCTL, which concerns fidelity instead of probability in probabilistic CTL, over quantum Markov chains (QMCs). Noisy channels are modelled by super-operators, which are specified by QCTL formulas; the initial quantum states are modelled by density operators, which are left parametric in the given QMC. The problem is to compute the minimumfidelity over all initial states for conservation. We achieve it by a reduction to quantifier elimination in the existential theory of the reals. The method is absolutely exact, so that QCTL formulas are proven to be decidable in exponential time. Finally, we implement the proposed method and demonstrate its effectiveness via a quantum IPv4 protocol

An academic achievements visualization research since the 21st century: research on salvage surgery for head and neck cancer

BackgroundHead and neck cancer is the 6th most common malignancy worldwide, and its incidence is still on the rise. The salvage surgery has been considered as an important treatment strategy for persistent or recurrent head and neck cancer. Therefore, we conducted a bibliometric analysis of salvage surgery for head and neck cancer since the 21st century.MethodsThe literature about salvage surgery of head and neck cancer in Web of Science was searched. CiteSpace and VOSviewer were used to analyze main countries, institutions, authors, journals, subject hotspots, trends, frontiers, etc.ResultsA total of 987 papers have been published since the 21st century. These publications were written by 705 authors from 425 institutions in 54 countries. The United States published 311 papers in this field and ranked first. Head & Neck was the most widely published journal. The main keyword clustering included terms such as #0 stereotactic radiotherapy (2012); #1 randomized multicenter (2007); #2 salvage surgery (2004); #3 functional outcomes (2014); #4 transoral robotic surgery (2013); #5 neck high-resolution computed tomography (2010); #6 complications (2008); #7 image guidance (2019). The current research frontiers that have been sustained are “recurrent”, “risk factors”, and “reirradiation”.ConclusionThe current situation of the salvage surgery for head and neck cancer in clinical treatments and basic scientific research were summarized, providing new perspectives for the development of salvage surgery for head and neck cancer in the future

Improving Detection in Aerial Images by Capturing Inter-Object Relationships

In many image domains, the spatial distribution of objects in a scene exhibits meaningful patterns governed by their semantic relationships. In most modern detection pipelines, however, the detection proposals are processed independently, overlooking the underlying relationships between objects. In this work, we introduce a transformer-based approach to capture these inter-object relationships to refine classification and regression outcomes for detected objects. Building on two-stage detectors, we tokenize the region of interest (RoI) proposals to be processed by a transformer encoder. Specific spatial and geometric relations are incorporated into the attention weights and adaptively modulated and regularized. Experimental results demonstrate that the proposed method achieves consistent performance improvement on three benchmarks including DOTA-v1.0, DOTA-v1.5, and HRSC 2016, especially ranking first on both DOTA-v1.5 and HRSC 2016. Specifically, our new method has an increase of 1.59 mAP on DOTA-v1.0, 4.88 mAP on DOTA-v1.5, and 2.1 mAP on HRSC 2016, respectively, compared to the baselines

Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling, practically limiting the development of the field. In this paper, we take the initiative to explore and propose a generic paradigm termed Superpixel-based Interaction Learning (SIL) to remedy coarse-grained interactions at the box level. It allows us to model fine-grained interactions at the superpixel level in SGG. Specifically, (i) we treat a scene as a set of points and cluster them into superpixels representing sub-regions of the scene. (ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage. Extensive experiments on two challenging benchmarks (Visual Genome and Open Image V6) prove that our SIL enables fine-grained interaction at the superpixel level above previous box-level methods, and significantly outperforms previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing box-level approaches in a plug-and-play fashion. In particular, SIL brings an average improvement of 2.0% mR (even up to 3.4%) of baselines for the PredCls task on Visual Genome, which facilitates its integration into any existing box-level method

Feedback RoI Features Improve Aerial Object Detection

Neuroscience studies have shown that the human visual system utilizes high-level feedback information to guide lower-level perception, enabling adaptation to signals of different characteristics. In light of this, we propose Feedback multi-Level feature Extractor (Flex) to incorporate a similar mechanism for object detection. Flex refines feature selection based on image-wise and instance-level feedback information in response to image quality variation and classification uncertainty. Experimental results show that Flex offers consistent improvement to a range of existing SOTA methods on the challenging aerial object detection datasets including DOTA-v1.0, DOTA-v1.5, and HRSC2016. Although the design originates in aerial image detection, further experiments on MS COCO also reveal our module's efficacy in general detection models. Quantitative and qualitative analyses indicate that the improvements are closely related to image qualities, which match our motivation

Ultrasound modulates ion channel currents

Transcranial focused ultrasound (US) has been demonstrated to stimulate neurons in animals and humans, but the mechanism of this effect is unknown. It has been hypothesized that US, a mechanical stimulus, may mediate cellular discharge by activating mechanosensitive ion channels embedded within cellular membranes. To test this hypothesis, we expressed potassium and sodium mechanosensitive ion channels (channels of the two-pore-domain potassium family (K2P) including TREK-1, TREK-2, TRAAK; Na(V)1.5) in the Xenopus oocyte system. Focused US (10 MHz, 0.3–4.9 W/cm(2)) modulated the currents flowing through the ion channels on average by up to 23%, depending on channel and stimulus intensity. The effects were reversible upon repeated stimulation and were abolished when a channel blocker (ranolazine to block Na(V)1.5, BaCl(2) to block K2P channels) was applied to the solution. These data reveal at the single cell level that focused US modulates the activity of specific ion channels to mediate transmembrane currents. These findings open doors to investigations of the effects of  US on ion channels expressed in neurons, retinal cells, or cardiac cells, which may lead to important medical applications. The findings may also pave the way to the development of sonogenetics: a non-invasive, US-based analogue of optogenetics

Towards Benchmarking and Evaluating Deepfake Detection

Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos. It is natural to ask which are the top performers among the existing deepfake detection approaches to identify promising research directions and provide practical guidance. Unfortunately, it's difficult to conduct a sound benchmarking comparison of existing detection approaches using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to establish a comprehensive and consistent benchmark, to develop a repeatable evaluation procedure, and to measure the performance of a range of detection approaches so that the results can be compared soundly. A challenging dataset consisting of the manipulated samples generated by more than 13 different methods has been collected, and 11 popular detection approaches (9 algorithms) from the existing literature have been implemented and evaluated with 6 fair-minded and practical evaluation metrics. Finally, 92 models have been trained and 644 experiments have been performed for the evaluation. The results along with the shared data and evaluation methodology constitute a benchmark for comparing deepfake detection approaches and measuring progress

Evolution and impact of molecular glue research: a bibliometric analysis from 2000 to 2023

BackgroundMolecular glues, which reshape E3 ligase receptors to promote targeted protein degradation, are emerging as a promising therapeutic strategy, particularly in oncology, driven by rapidly advancing insights into their mechanisms and structural properties.ObjectiveThis study aims to offer an insightful depiction and visualization of the knowledge structure, prevalent themes, and emerging trends within the domain since the year 2000, employing bibliometric analysis to achieve this goal.MethodsTo conduct this research, a comprehensive collection of literature on molecular glues was sourced from the Web of Science database. Subsequently, the data underwent analysis utilizing CiteSpace and VOSviewer tools, enabling the identification of pivotal countries, institutions, authors, and journals, as well as the delineation of subject hotspots, trends, and the forefront of research in this evolving field.ResultSince 2000, 388 papers on molecular glues have been published, with a notable increase to an annual average of 43 articles post-2018. This research, contributed by 506 authors across 329 institutions, highlights the United States and China as leading nations in output, with 122 and 104 articles respectively. Takuzo Aida, Luc Brunsveld, and Christian Ottmann are identified as key authors. Nature emerges as the foremost publication venue, while the Chinese Academy of Sciences is the top contributing institution, underscoring the global engagement and interdisciplinary nature of molecular glue research. This study identified 19 distinct research clusters within the molecular glues domain.ConclusionWe reveal the current status, hotspots, and trends of molecular glue research since 2000, offering insights and novel scholarly perspectives on the field’s prevailing limitations