Reviewerly/exHarmony huggingface dataset:
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

As a fundamental component of autonomous driving systems, motion planning has garnered significant attention from both academia and industry. This paper focuses on efficient and spatial-temporal optimal trajectory optimization in unstructured environments using compact convex approximations of vehicle shapes. Conventional approaches typically model the task as an optimal control problem by discretizing the motion process in state configuration space. However, this often results in a tradeoff between optimality and efficiency since generating high-quality motion trajectories often requires high-precision discretization of the dynamic process, which imposes a substantial computational burden. To address this issue, we leverage the differential flatness property of car-like robots to simplify the trajectory representation and analytically formulate the spatial-temporal joint optimization problem with flat outputs in a compact manner, while ensuring the feasibility of nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a collision-free driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.
Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies.
A deep model towards accurate boundary location and strong generalization for medical image segmentation

Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis and monitoring. However, due to the complexity of medical images and the limitations of image acquisition, most of the current segmentation models still face the challenges of imprecise boundary positioning and poor generalization ability, which greatly affect their application in clinical practices. To address these issues, we propose a new deep learning-based medical image semantic segmentation network (BLG-Net). To improve the accuracy of object boundary location, a Local–global module (Logo) is proposed. It uses the combination of a self-attention mechanism and a cross-attention mechanism to model intra-sample and inter-sample long-range dependencies from local and global perspectives, respectively, to help recover the spatial structures. To further enhance the generalization ability of the BLG-Net, we develop a generalization enhancement module (GEM), which can adaptively adjust the classifier weights of the network according to the features of the test images extracted during the test process to alleviate the distribution shift issues. Besides, we design a multi-granularity loss (MGL) function, which can guide the network to learn the object region and its boundary details at the pixel, patch, and map levels, respectively. Extensive experiments on five medical image segmentation tasks demonstrate the improvements of our method. We further verify the model’s generalization ability to unseen data on two other popular datasets with different modalities.
A new approach of contrast enhancement for Medical Images based on entropy curve

X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.
Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial–temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high efficiency.
NeatIBP 1.0, a package generating small-size integration-by-parts relations for Feynman integrals

In this work, we present the package NeatIBP, which automatically generates small-size integration-by-parts (IBP) identities for Feynman integrals. Based on the syzygy and module intersection techniques, the generated IBP identities' propagator degree is controlled and thus the size of the system of IBP identities is shorter than that generated by the standard Laporta algorithm. This package is powered by the computer algebra systems Mathematica and Singular, and the library SpaSM. It is parallelized on the level of Feynman integral sectors. The generated small-size IBP identities can subsequently be used for either finite field reduction or analytic reduction. We demonstrate the capabilities of this package on several multi-loop IBP examples. Program Title: NeatIBP CPC Library link to program files: https://doi.org/10.17632/ms85fpfm7b.1 Developer's repository link: https://github.com/yzhphy/NeatIBP Licensing provisions: GPLv3 Programming language: Mathematica, Singular, C Nature of problem: The difficulty of the IBP reduction of Feynman integrals stems from the large size of the IBP system. Solution method: We apply the module intersection method to generate IBP relations by algebraic geometry methods, and compare the smaller IBP system we generate with that from Laporta's algorithm. NeatIBP generates the module intersection in Singular [1] and selects relevant and independent IBP relations via an algorithm relying on the library SpaSM [2]. The workflow is parallelized by a task manager written in Mathematica [3]. W. Decker, G.-M. Greuel, G. Pfister, and H. Schönemann, Singular 4-3-1 – A computer algebra system for polynomial computations, http://www.singular.uni-kl.de, 2022. The SpaSM group, SpaSM: a Sparse direct Solver Modulo p, http://github.com/cbouilla/spasm, 2017. Wolfram Research, Mathematica 13.2, https://www.wolfram.com/mathematica/.
Light-field 3D image parallel encryption based on the state transition diagram of Maximum Length Cellular Automata

In our work, we proposed a parallel encryption scheme based on the state transition diagram of Maximum Length Cellular Automata (MLCA) to protect 3D images. Compared with the previous random mode decomposition (RMD) encryption scheme and DNA sequences and chaotic systems scheme, our scheme effectively reduces the time consumption, especially for the RME scheme, by up to 5 times. Meanwhile, leveraging MLCA's abundant rules, high parallelism, and straightforward hardware implementation, our encryption scheme exhibits flexible key selection, parallel processing capabilities, and easy hardware implementation. The results of key space analysis, differential attack analysis, and statistical analysis verify the high-security level achieved by our proposed parallel encryption scheme.
A dynamic state sharding blockchain architecture for scalable and secure crowdsourcing systems

Currently, the crowdsourcing system has serious problems such as single point of failure of the server, leakage of user privacy, unfair arbitration, etc. By storing the interactions between workers, requesters, and crowdsourcing platforms in the form of transactions on the blockchain, these problems can be effectively addressed. However, the improvement in total computing power on the blockchain is difficult to provide positive feedback to the efficiency of transaction confirmation, thereby limiting the performance of crowdsourcing systems. On the other hand, the increasing amount of data in blockchain further increases the difficulty of nodes participating in consensus, affecting the security of crowdsourcing systems. To address the above problems, in this paper we design a blockchain architecture based on dynamic state sharding, called DSSBD. Firstly, we solve the problems caused by cross sharding transactions and reconfiguration in blockchain state sharding through graph segmentation and relay transactions. Then, we model the optimal block generation problem as a Markov decision process. By utilizing deep reinforcement learning, we can dynamically adjust the number of shards, block spacing, and block size. This approach helps improve both the throughput of the blockchain and the proportion of non-malicious nodes. Security analysis has proven that the proposed DSSBD can effectively resist attacks such as transaction atomic attacks, double spending attacks, sybil attacks, replay attacks, etc. The experimental results show that the crowdsourcing system with the proposed DSSBD has better performance in throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, etc., while ensuring security.
Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.
APGVAE: Adaptive disentangled representation learning with the graph-based structure information

Neural networks are used to learn task-oriented high-level representations in an end-to-end manner by building a multi-layer neural network. Generation models have developed rapidly with the emergence of deep neural networks. But it still has problems with the insufficient authenticity of generated images, the deficiency of diversity, consistency, and unexplainability in the generation process. Disentangled representation is an effective method to learn a high-level feature representation and realize the interpretability of deep neural networks. We propose a general disentangled representation learning network with variational autoencoder network as the basic framework for the image generation process. The graph-based structure of the priors is embedded in the last module of the deep encoder network to build the feature spaces by the class, task-oriented, and task-unrelated information respectively. Meanwhile the priors should be adaptively modified with the task relevance of a generated image. And the semi-supervised learning is further involved in the disentangled representation network framework to reduce the requirements of label and extend the majority of feature space under the task-unrelated feature assumption. Experimental results show that the proposed method is efficient for various types of images and has a good potential for further research and development.
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning

Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy. The success of FL hinges on the efficiency of participating models and their ability to handle the unique challenges of distributed learning. While several variants of Vision Transformer (ViT) have shown great potential as alternatives to modern convolutional neural networks (CNNs) for centralized training, the unprecedented size and higher computational demands hinder their deployment on resource-constrained edge devices, challenging their widespread application in FL. Since client devices in FL typically have limited computing resources and communication bandwidth, models intended for such devices must strike a balance between model size, computational efficiency, and the ability to adapt to the diverse and non-IID data distributions encountered in FL. To address these challenges, we propose OnDev-LCT: Lightweight Convolutional Transformers for On-Device vision tasks with limited training data and resources. Our models incorporate image-specific inductive biases through the LCT tokenizer by leveraging efficient depthwise separable convolutions in residual linear bottleneck blocks to extract local features, while the multi-head self-attention (MHSA) mechanism in the LCT encoder implicitly facilitates capturing global representations of images. Extensive experiments on benchmark image datasets indicate that our models outperform existing lightweight vision models while having fewer parameters and lower computational demands, making them suitable for FL scenarios with data heterogeneity and communication bottlenecks.
An intelligent correlation learning system for person Re-identification

Person re-identification (PRe-id) aims to retrieve a target person's images captured across multiple/single non-overlapping cameras. To this end, significant techniques have been implemented that extract handcrafted, deep, part-based, and ensemble features to get more refined patterns for matching. But due to the limited focus on the multi-grained, view-consistent, and semantic correlation among different views, these approaches show low performance. Therefore, we present an attention-based multi-view correlation learning framework named (ACLS), which enables to learn multi-grained spatiotemporal features from individuals. The ACLS is mainly composed of three key steps: First, multi-view correlated visual features of pedestrians are extracted using a correlation vision transformer (CVIT) and a pyramid dilated network (PDN), followed by the person attention mechanism. Next, we employ convolutional long short-term memory (ConvLSTM) to extract spatiotemporal information from pedestrian images captured in different time frames. Finally, a deep fusion strategy is employed, which intelligently integrates features for the final matching task. Extensive evaluations are conducted over three famous datasets: Market-1501, DukeMCMT-reID, and CUHK03, results show tremendous ranking performance including 93.7%, 90.4%, and 85.7%. Thus, concluded remarks that our learning mechanism beats the current state-of-the-art (SOTA) methods.
Evaluation of solar PV-based microgrids viability utilizing single and multi-criteria decision analysis

Feasibility analysis is crucial for solar projects to minimize risk and ensure goal achievement. Essential factors include the PV system's location, elevation, land slope, energy yield, soil texture, mean annual cloud frequency map, and distance from urban areas. This study examines the performance of a 3 MW PV system across five remote Egyptian regions characterized by desert weather conditions. The influence of available weather data sources and different yield estimation methods on the feasibility of the PV system is treated as a Single-Criteria Decision Making (SCDM) scheme. Meteorological data from Meteonorm 8.1, PVGIS, and NASA-SSE satellite data are utilized as input data for the designed PVsyst and ANSYS-based energy yield estimation models and for empirical formulas. Significant variations in estimated energy yield are observed, depending on the weather data source. The study also underscores the importance of diligently evaluating and comparing the accuracy and reliability of different weather data sources and estimation methods for simulating PV-based microgrid system performance. The yield estimation-based SCDM indicates that the New Valley PV system consistently outperformed the other systems. However, a Multi-Criteria Decision Making (MCDM) indicates that the Red Sea performs well in technical criteria evaluation, while alternative Aswan City excels in practical and feasibility criteria assessment. The study employs the MCDM approach to provide a more detailed evaluation. Specifically, the technical criteria focus on energy yield, system efficiency, and reliability. In contrast, a decision-maker prioritizing long-term sustainability might lean towards a system that excels in technical criteria, even if it is economically less attractive in the short term. Conversely, a decision-maker constrained by budget limitations might opt for a system that, while not the most technically efficient, offers the most cost-effective solution. This dual-criteria approach reveals that while one region may excel in technical performance, another is more financially viable. The choice between these two regions hinges on the specific priorities of the decision-maker.
Large language models (LLMs) in the evaluation of emergency radiology reports: performance of ChatGPT-4, Perplexity, and Bard

Large language models (LLMs), especially those based on the Generative Pre-trained Transformer (GPT) architecture, have become widely popular and have been applied in various fields due to their ability to provide written responses to a diverse range of queries swiftly and accurately. LLMs have demonstrated a transformative and potentially revolutionary capacity in multiple medical subfields, including radiology.1
Secret sharing: A comprehensive survey, taxonomy and applications

The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day's major challenge. Secret sharing is a method that allows a trusted authority (the dealer) to distribute a secret or a number of secrets among some target participants with the intention that certain predetermined groups of participants can collaborate to recover the secret or secrets. Any other group formed by the participants cannot do so. Threshold secret sharing (TSS) is a particular form of secret sharing. It permits any group consisting of at least a specific number (called the threshold) of participants to reconstruct the secret or secrets. However, any group with fewer than the specified number of participants is forbidden to do so. It provides tolerance against single point of failure (SPOF), which has attracted a large number of researchers to contribute in this field. It has the potential to be implemented in numerous practical and secure applications. In this paper, we present a comprehensive survey of a variety of existing threshold secret sharing schemes. We have identified various aspects of developing secure and efficient secret sharing schemes. We have also highlighted some of the applications based on secret sharing. Finally, the open challenges and future research directions in the field of secret sharing are identified and discussed.