Redefining cystoscopy with ai: bladder cancer diagnosis using an efficient hybrid cnn-transformer model

Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors' expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and treated as urinary infections. To address this, we suggest a deep learning approach for bladder cancer detection and segmentation which combines CNNs with a lightweight positional-encoding-free transformer and dual attention gates that fuse self and spatial attention for feature enhancement. The architecture suggested in this paper is efficient making it suitable for medical scenarios that require real time inference. Experiments have proven that this model addresses the critical need for a balance between computational efficiency and diagnostic accuracy in cystoscopic imaging as despite its small size it rivals large models in performance.

Comparison Performance of Spectrogram and Scalogram as Input of Acoustic Recognition Task

Acoustic recognition is a common task for deep learning in recent researches, with the employment of spectral feature extraction such as Short-time Fourier transform and Wavelet transform. However, not many researches have found that discuss the advantages and drawbacks, as well as performance comparison amongst spectral feature extractors. In this consideration, this paper aims to comparing the attributes of these two transform types, called spectrogram and scalogram. A Convolutional Neural Networks for acoustic faults recognition is implemented, then the performance of these two types of spectral extractor is recorded for comparison. A latest research on the same audio database is considered for benchmarking to see how good the designed spectrogram and scalogram is. The advantages and limitations of them are also analyzed. By doing so, the results of this paper provide indications for application scenarios of spectrogram and scalogram, as well as potential further research directions in acoustic recognition.

Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.

Maximizing Energy Charging for UAV-assisted MEC Systems with SWIPT

A Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) scheme with simultaneous wireless information and power transfer (SWIPT) is proposed in this paper. Unlike existing MEC-WPT schemes that disregard the downlink period for returning computing results to the ground equipment (GEs), our proposed scheme actively considers and capitalizes on this period. By leveraging the SWIPT technique, the UAV can simultaneously transmit energy and the computing results during the downlink period. In this scheme, our objective is to maximize the remaining energy among all GEs by jointly optimizing computing task scheduling, UAV transmit and receive beamforming, BS receive beamforming, GEs' transmit power and power splitting ratio for information decoding, time scheduling, and UAV trajectory. We propose an alternating optimization algorithm that utilizes the semidefinite relaxation (SDR), singular value decomposition (SVD), and fractional programming (FP) methods to effectively solve the nonconvex problem. Numerous experiments validate the effectiveness of the proposed scheme.

A Survey on Temporal Knowledge Graph: Representation Learning and Applications

Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.

Advancing Biomedical Text Mining with Community Challenges

The field of biomedical research has witnessed a significant increase in the accumulation of vast amounts of textual data from various sources such as scientific literatures, electronic health records, clinical trial reports, and social media. However, manually processing and analyzing these extensive and complex resources is time-consuming and inefficient. To address this challenge, biomedical text mining, also known as biomedical natural language processing, has garnered great attention. Community challenge evaluation competitions have played an important role in promoting technology innovation and interdisciplinary collaboration in biomedical text mining research. These challenges provide platforms for researchers to develop state-of-the-art solutions for data mining and information processing in biomedical research. In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining. Firstly, we collect the information of these evaluation tasks, such as data sources and task types. Secondly, we conduct systematic summary and comparative analysis, including named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. Then, we summarize the potential clinical applications of these community challenge tasks from translational informatics perspective. Finally, we discuss the contributions and limitations of these community challenges, while highlighting future directions in the era of large language models.

Optical turbulence profiling at the Table Mountain Facility with the Laser Communication Relay Demonstration GEO downlink

We present the first time the profile of atmospheric optical turbulence has been measured using the transmitted beam from a satellite laser communication terminal. A Ring Image Next Generation Scintillation Sensor (RINGSS) instrument for turbulence profiling, as described in Tokovinin (MNRAS, 502.1, 2021), was deployed at the NASA/Jet Propulsion Laboratory's Table Mountain Facility (TMF) in California. The optical turbulence profile was measured with the downlink optical beam from the Laser Communication Relay Demonstration (LCRD) Geostationary satellite. LCRD conducts links with the Optical Communication Telescope Laboratory ground station and the RINGSS instrument was co-located at TMF to conduct measurements. Turbulence profiles were measured at day and night and atmospheric coherence lengths were compared with other turbulence monitors such as a solar scintillometer and Polaris monitor. RINGSS sensitivity to boundary layer turbulence, a feature not provided by many profilers, is also shown to agree well with a boundary layer scintillometer at TMF. Diurnal evolution of optical turbulence and measured profiles are presented. The robust correlation of RINGSS with other turbulence monitors demonstrates the concept of free-space optical communications turbulence profiling, which could be adopted as a way to support optical ground stations in a future Geostationary feeder link network. These results also provide further evidence that RINGSS, a relatively new instrument concept, is effective even in strong daytime turbulence and with reasonable ground layer sensitivity.

Fixture calibration with guaranteed bounds from a few correspondence-free surface points

Calibration of fixtures in robotic work cells is essential but also time consuming and error-prone, and poor calibration can easily lead to wasted debugging time in downstream tasks. Contact-based calibration methods let the user measure points on the fixture's surface with a tool tip attached to the robot's end effector. Most such methods require the user to manually annotate correspondences on the CAD model, however, this is error-prone and a cumbersome user experience. We propose a correspondence-free alternative: The user simply measures a few points from the fixture's surface, and our method provides a tight superset of the poses which could explain the measured points. This naturally detects ambiguities related to symmetry and uninformative points and conveys this uncertainty to the user. Perhaps more importantly, it provides guaranteed bounds on the pose. The computation of such bounds is made tractable by the use of a hierarchical grid on SE(3). Our method is evaluated both in simulation and on a real collaborative robot, showing great potential for easier and less error-prone fixture calibration. Project page at https://sites.google.com/view/ttpose

Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Views

Novel-view synthesis with sparse input views is important for real-world applications like AR/VR and autonomous driving. Recent methods have integrated depth information into NeRFs for sparse input synthesis, leveraging depth prior for geometric and spatial understanding. However, most existing works tend to overlook inaccuracies within depth maps and have low time efficiency. To address these issues, we propose a depth-guided robust and fast point cloud fusion NeRF for sparse inputs. We perceive radiance fields as an explicit voxel grid of features. A point cloud is constructed for each input view, characterized within the voxel grid using matrices and vectors. We accumulate the point cloud of each input view to construct the fused point cloud of the entire scene. Each voxel determines its density and appearance by referring to the point cloud of the entire scene. Through point cloud fusion and voxel grid fine-tuning, inaccuracies in depth values are refined or substituted by those from other views. Moreover, our method can achieve faster reconstruction and greater compactness through effective vector-matrix decomposition. Experimental results underline the superior performance and time efficiency of our approach compared to state-of-the-art baselines.

Analysis and Fully Memristor-based Reservoir Computing for Temporal Data Classification

Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared to conventional recurrent neural networks. A key component in its hardware deployment is the ability to generate dynamic reservoir states. Our research introduces a novel dual-memory RC system, integrating a short-term memory via a WOx-based memristor, capable of achieving 16 distinct states encoded over 4 bits, and a long-term memory component using a TiOx-based memristor within the readout layer. We thoroughly examine both memristor types and leverage the RC system to process temporal data sets. The performance of the proposed RC system is validated through two benchmark tasks: isolated spoken digit recognition with incomplete inputs and Mackey-Glass time series prediction. The system delivered an impressive 98.84% accuracy in digit recognition and sustained a low normalized root mean square error (NRMSE) of 0.036 in the time series prediction task, underscoring its capability. This study illuminates the adeptness of memristor-based RC systems in managing intricate temporal challenges, laying the groundwork for further innovations in neuromorphic computing.