Static Information Research Articles

Illuminating immunotherapy response via precision T cell-targeted PET imaging.

Traditionally, immunotherapy agent selection and treatment strategies are guided by biopsy-based histological information. However, biopsies are limited in that they are invasive, provide static information regarding the tumor immune microenvironment, and only sample a small part of one tumor site. The tumor microenvironment is dynamic and heterogenous. As a result, the immune milieu at one site may be distinct from other metastatic sites. These factors make identifying which patients are likely to respond to different immunotherapies and which harbor intrinsic resistance mechanisms difficult to identify based on a biopsy alone. As such, there is significant interest in alternative methodologies that better characterize the tumor immune microenvironment and monitor immunotherapy response. PET imaging potentially offers a non-invasive way to characterize the tumor immune microenvironment at the primary tumor and metastases and allow for longitudinal characterization. Herein, we review pre-clinically and clinically tested T cell-targeted PET radiopharmaceuticals, as T cells have been the dominant immunotherapy target, and their utility in both evaluating response to immunotherapy and in understanding the systemic immune response to treatment with immunotherapeutics.

HIV-1-envelope trimer transitions from prefusion-closed to CD4-bound-open conformations through an occluded-intermediate state.

HIV-1 infection is initiated by the interaction between the gp120 subunit in the envelope (Env) trimer and the cellular receptor CD4 on host cells. This interaction induces substantial structural rearrangement of the Env trimer. Currently, static structural information for prefusion-closed trimers, CD4-bound prefusion-open trimers, and various antibody-bound trimers is available. However, dynamic features between these static states (e.g., transition structures) are not well understood. Here, we investigate the full transition pathway of a site specifically glycosylated Env trimer between prefusion-closed and CD4-bound-open conformations by collective molecular dynamics and single-molecule Förster resonance energy transfer (smFRET). Our investigations reveal and confirm important features of the transition pathway, including movement of variable loops to generate a glycan hole at the trimer apex and formation or rearrangements of α-helices and β-strands. Notably, by comparing the transition pathway to known Env-structures, we uncover evidence for a transition intermediate, with four antibodies, Ab1303, Ab1573, b12, and DH851.3, recognizing this intermediate. Each of these four antibodies induce population shifts of Env to occupy a newly observed smFRET state: the "occluded-intermediate" state. We propose this occluded-intermediate state to be both a prevalent state of Env and an on-path conformation between prefusion-closed and CD4-bound-open states, previously overlooked in smFRET analyses.

DILA: Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment for tiny object detection

With the rapid advancement of deep learning technology, significant achievements have been made in the field of general object detection. However, challenges still remain in the detection of tiny objects. There are two main drawbacks: (1) static prior information makes the localization of tiny objects relatively fixed; (2) Intersection over Union (IoU) is highly sensitive to deviations in tiny objects. To this end, we propose a Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment (DILA) strategy for Tiny Object Detection. Specifically, to address the positional deviation of effective receptive fields at different network layers during Gaussian modeling, DILA first designs an Adaptive Dynamic Calculation Strategy (ADCS) to compute estimation factors for the effective receptive field in different feature spaces, dynamically modeling prior information using Gaussian distribution. Then, DILA introduces a new Balance of Gaussian Scaling-aware Metric (BGSM) to measure the similarity between tiny bounding boxes and predefined anchors, instead of using IoU, which is highly sensitive to tiny pixel shifts, for sample assignment, thereby providing a more accurate basis for label assignment. Finally, a Detail Information Imitation Compensation Module (DIM) is presented to improve and compensate for the detailed information of tiny objects that troubles label assignment, achieving balanced learning for tiny objects. The proposed DILA strategy can be seamlessly integrated into various anchor-based detectors. Extensive experiments were conducted on three publicly available datasets for tiny object detection. The results indicate that when DILA is embedded into Faster RCNN, it outperforms other state-of-the-art methods in terms of detection performance for tiny objects, achieving an improvement in average precision of 10.3%, 1.5%, and 4.2% on AI-TOD, SODA-D, and VisDrone2019, respectively. The source codes and results are available at: https://github.com/chnu-cpl/DILA.

Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow

Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on the ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. Second, the static environment occluded by the dynamic target is repaired using the time-weighted multi-frame fusion background repair technique. Lastly, the filtered static feature points are used for feature matching and position calculation. Meanwhile, the semantic labeling information of static objects obtained based on the lightweight semantic segmentation network DeepLabV3+ is fused with the static environment information after background repair to generate dense point cloud maps containing semantic information, and the semantic dense point cloud maps are transformed into semantic octree maps using the octree spatial segmentation data structure. The localization accuracy of the visual SLAM system and the construction of the semantic maps are verified using the widely used TUM RGB-D dataset and real scene data, respectively. The experimental results show that the proposed semantic visual SLAM algorithm can effectively reduce the influence of dynamic targets on the system, and compared with other advanced algorithms, such as DynaSLAM, it has the highest performance in indoor dynamic environments in terms of localization accuracy and time consumption. In addition, semantic maps can be constructed so that the robot can better understand and adapt to the indoor dynamic environment.

Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies.

Despite the vast potential for insights and value present in Electronic Health Records (EHRs), it is challenging to fully leverage all the available information, particularly that contained in the free-text data written by clinicians describing the health status of patients. The utilization of Named Entity Recognition and Linking tools allows not only for the structuring of information contained within free-text data, but also for the integration with medical ontologies, which may prove highly beneficial for the analysis of patient medical histories with the aim of forecasting future medical outcomes, such as the diagnosis of a new disorder. In this paper, we propose MedTKG, a Temporal Knowledge Graph (TKG) framework that incorporates both the dynamic information of patient clinical histories and the static information of medical ontologies. The TKG is used to model a medical history as a series of snapshots at different points in time, effectively capturing the dynamic nature of the patient's health status, while a static graph is used to model the hierarchies of concepts extracted from domain ontologies. The proposed method aims to predict future disorders by identifying missing objects in the quadruple 〈s, r, ?, t 〉, where s and r denote the patient and the disorder relation type, respectively, and t is the timestamp of the query. The method is evaluated on clinical notes extracted from MIMIC-III and demonstrates the effectiveness of the TKG framework in predicting future disorders and of medical ontologies in improving its performance.

Sensorimotor prediction is used to direct gaze toward task-relevant locations in a goal-directed throwing task.

Previous research has shown that action effects of self-generated movements are internally predicted before outcome feedback becomes available. To test whether these sensorimotor predictions are used to facilitate visual information uptake for feedback processing, we measured eye movements during the execution of a goal-directed throwing task. Participants could fully observe the effects of their throwing actions (ball trajectory and either hitting or missing a target) in most of the trials. In a portion of the trials, the ball trajectory was not visible, and participants only received static information about the outcome. We observed a large proportion of predictive saccades, shifting gaze toward the goal region before the ball arrived and outcome feedback became available. Fixation locations after predictive saccades systematically covaried with future ball positions in trials with continuous ball flight information, but notably also in trials with static outcome feedback and only efferent and proprioceptive information about the movement that could be used for predictions. Fixation durations at the chosen positions after feedback onset were modulated by action outcome (longer durations for misses than for hits) and outcome uncertainty (longer durations for narrow vs. clear outcomes). Combining both effects, durations were longest for narrow errors and shortest for clear hits, indicating that the chosen locations offer informational value for feedback processing. Thus, humans are able to use sensorimotor predictions to direct their gaze toward task-relevant feedback locations. Outcome-dependent saccade latency differences (miss vs. hit) indicate that also predictive valuation processes are involved in planning predictive saccades.NEW & NOTEWORTHY We elucidate the potential benefits of sensorimotor predictions, focusing on how the system actually uses this information to optimize feedback processing in goal-directed actions. Sensorimotor information is used to predict spatial parameters of movement outcomes, guiding predictive saccades toward future action effects. Saccade latencies and fixation durations are modulated by outcome quality, indicating that predictive valuation processes are considered and that the locations chosen are of high informational value for feedback processing.

Tiny drone object detection in videos guided by the bio-inspired magnocellular computation model

Detecting drones in infrared videos is highly desired in many realistic scenarios, e.g., unauthorized drone monitoring around airports. Nevertheless, automated drone detection is rather challenging when the targets appear as tiny objects (≤10×10 pixels) against complex backgrounds. Conventional object detection algorithms, which mainly use static visual features, can hardly distinguish tiny objects from undesired artefacts in complex backgrounds. To alleviate this problem, we learn from the early biological visual pathway (including the parvocellular and magnocellular pathways), which process static and motion information simultaneously. Therefore, we propose a magnocellular inspired method for video tiny-object detection (Magno-VTOD) that integrates both static and motion visual information. The Magno-VTOD firstly employs a retinal magnocellular computation model to extract the motion strength of moving objects. The motion responses are then used to enhance the areas of the flying tiny drones effectively and efficiently, thereby facilitating the subsequent target detection procedure. We implement the video tiny-object detection method based on the widely adopted deep neural networks guided by the magnocellular computation model. Experimental results obtained on the large-scale Anti-UAV dataset (304451 video frames) validate that the proposed Magno-VTOD method significantly outperforms the competing state-of-the-art object detection methods on the tiny drone detection task. Particularly, the AP value is increased by 15.4% for tiny object detection, and by 17.1%/13.7% against wood/mountain backgrounds.

Stories behind decisions: Towards interpretable malware family classification with hierarchical attention

Malware family classification is essential for understanding malware code and behavioral characteristics, significantly reducing analysis time and response time for emerging malware. However, existing malware family classification methods are limited for two reasons. (1) The feature representation learned from a single information source is not sufficiently discriminative for large-scale malware family classification. (2) The lack of interpretability of classification results makes it difficult to provide intuitive guidance for malware analysis. To tackle the issues, we propose MalAtt, a novel malware family classification method for interpretable and large-scale classification. Specifically, a unified feature engineering approach is proposed to obtain integrated embedding from heterogeneous static and dynamic information. Then, a hierarchical attention encoder module is employed to progressively extract semantic features from the static and dynamic embedding. A collaborative attention module is introduced to model the association between high-level static and dynamic features. Finally, MalAtt is pluggable for different classifiers, such as SVM and RF, to implement malware family classification. For evaluations, we carefully collected a dataset from 62 malware families containing over 20,070 in-the-wild samples. MalAtt is proved to be effective when classifying large-scale malware families, with an accuracy of 93.26%. Besides, the ablation study demonstrates the rationality of the proposed modules. Additionally, we provide a case study to show how MalAtt intuitively guides in-depth malware analysis by highlighting partial input features with high attention scores.

Behavioral threat and appeasement signals take precedence over static colors in lizard contests

Abstract The interplay between morphological (structures) and behavioral (acts) signals in contest assessment is still poorly understood. During contests, males of the common wall lizard (Podarcis muralis) display both morphological (i.e. static color patches) and behavioral (i.e. raised-body display, foot shakes) traits. We set out to evaluate the role of these putative signals in determining the outcome and intensity of contests by recording agonistic behavior in ten mesocosm enclosures. We find that contests are typically won by males with relatively more black coloration, which are also more aggressive. However, black coloration does not seem to play a role in rival assessment, and behavioral traits are stronger predictors of contest outcome and winner aggression than prior experience, morphology, and coloration. Contest intensity is mainly driven by resource- and self-assessment, with males probably using behavioral threat (raised-body displays) and de-escalation signals (foot shakes) to communicate their willingness to engage/persist in a fight. Our results agree with the view that agonistic signals used during contests are not associated with mutual evaluation of developmentally-fixed attributes, and instead animals monitor each other to ensure that their motivation is matched by their rival. We emphasize the importance of testing the effect of signals on receiver behavior and discuss that social recognition in territorial species may select receivers to neglect potential morphological signals conveying static information on sex, age, or intrinsic quality.

R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21Hz. A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400ms, the average RMSE and MAE are 0.0453 and 0.0361mm, respectively, which is better than other research methods. The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

BiSAMNet: A Novel Approach in Maritime Data Completion Using Deep Learning and NLP Techniques

In the extensive monitoring of maritime traffic, maritime management frequently encounters incomplete automatic identification system (AIS) data. This deficiency poses significant challenges to safety management, requiring effective methods to infer corresponding ship information. We tackle this issue using a classification approach. Due to the absence of a fixed road network at sea unlike on land, raw trajectories are difficult to convert and cannot be directly fed into neural networks. We devised a latitude–longitude gridding encoding strategy capable of transforming continuous latitude–longitude data into discrete grid points. Simultaneously, we employed a compression algorithm to further extract significant grid points, thereby shortening the encoding sequence. Utilizing natural language processing techniques, we integrate the Word2vec word embedding approach with our novel biLSTM self-attention chunk-max pooling net (biSAMNet) model, enhancing the classification of vessel trajectories. This method classifies targets into ship types and ship lengths within static information. Employing the Taiwan Strait as a case study and benchmarking against CNN, RNN, and methods based on the attention mechanism, our findings underscore our model’s superiority. The biSAMNet achieves an impressive trajectory classification F1 score of 0.94 in the ship category dataset using only five-dimensional word embeddings. Additionally, through ablation experiments, the effectiveness of the Word2vec pre-trained embedding layer is highlighted. This study introduces a novel method for handling ship trajectory data, addressing the challenge of obtaining ship static information when AIS data are unreliable.

Synthesizing 4D Magnetic Resonance Angiography From 3D Time-of-Flight Using Deep Learning: A Feasibility Study.

Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 menand two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.

A Practical probabilistic history matching framework for characterizing fracture network parameters of shale gas reservoirs

Simulating fluid flow in complex fracture systems encountered in subsurface systems, such as those encountered in tight or shale gas reservoirs, presents considerable challenges. Sophisticated numerical techniques and characterization approaches are required to integrate static and dynamic data from multiple sources to account for the intricate interplay between fractures and the surrounding rock matrix. A novel workflow using an indicator-based probability perturbation method (PPM) is applied to characterize uncertain discrete fracture network parameters. The posterior probability distributions of primary and secondary fracture transmissivity, aperture, length, height, and intensity of the secondary fracture are estimated according to the production (flow) histories. A case study based on the Horn River shale gas reservoir is presented. A pilot point scheme is formulated to update the distributions of P32L. The forward modelling entails upscaling each fracture model into a dual-porosity dual-permeability model and performing multiphase flow simulation. This approach produces an ensemble of history-matched discrete fracture and upscaled models by incorporating static and dynamic data. The results demonstrate the utility of the developed method for estimating secondary fracture parameters, which are not inferrable from other static information alone. The inference of both primary and secondary fractures offers a more comprehensive characterization of the fracture networks in tight or shale gas reservoirs. Integrating a pilot point parameterization technique and a sequential simulation approach into the PPM framework presents significant novelties in contributing to a comprehensive and effective method of simulating fluid flow in complex fracture systems, addressing the challenges associated with non-linear relationships between fracture model parameters and the corresponding flow response.

What We Know So Far about Transfer Pricing: a Bibliometric Analysis

Abstract The objective of this study was to provide comprehensive static information in the area of transfer pricing in order to assess the current situation and attempt to establish future research priorities. The data source used is the WoS database, where 788 different articles with 21,917 corresponding references were collected for the analysis. A bibliometric approach was applied using the VoSviewer software. In comparison to Kumar et al. (2021), who presented the results of the citation analysis of the articles found in the SCOPUS database, the authors performed a first-level analysis of the articles found in the WoS database, but also a second-level analysis, which was presented as a co-citation analysis. The primary similarity arises from the use of an identical methodology at the first level of analysing different sources, while the results of the co-citation analysis allow the formation of clusters for references, sources and authors, thus closing the obvious gap in the analysis of Kumar et al. (2021). The obtained results show two logical directions of continuation and development of the studied topic. The first direction allows future researchers to have a basic understanding of the breadth and depth of currently published works and studies on transfer pricing, while the development of the treated topic should be seen in the context of filling the gaps between transfer pricing and bibliometric analysis.

MaP-SGAN: Multi-anchor point siamese GAN for Wi-Fi CSI-based cross-domain gait recognition

With the pervasive growth of IoT, Wi-Fi CSI-based human gait recognition faces the challenge of maintaining model robustness in new environments. Current cross-domain methods often rely on symmetric data, restricting their practical utility. To overcome this limitation, we propose MaP-SGAN, an innovative model specifically designed to enhance gait recognition’s robustness in cross-domain scenarios. MaP-SGAN integrates three crucial components: (1) the Consistency Maintenance Module, utilizing a Siamese network to ensure latent vector consistency within the asymmetric data domain; (2) the Diversity Enhancement Module, incorporating static environmental information to diversify the generator and improve the alignment of generated samples with the target domain’s characteristics; (3) the Multi-anchor Point Metrics in the discriminator, employing multi-anchor point metrics to constrain the similarity between generated data and target domain features. This metric approach comprehensively addresses the distributional differences between the source and target domains, thereby enhancing the model’s discriminative capability and generalization performance. Through extensive testing in diverse scenarios involving different clothing and environments, our proposed MaP-SGAN model achieves an average accuracy of 87.58% in cross-clothing recognition, which is an improvement of 4.03% compared to existing state-of-the-art models. The average accuracy in cross-environment recognition is 53.8%, which is an improvement of about 6.34% compared to existing state-of-the-art models.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device’s superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

UISA: User Information Separating Architecture for Commodity Recommendation Policy with Deep Reinforcement Learning

Commodity recommendation contributes an important part of individuals’ daily life. In this context, deep reinforcement learning methods have demonstrated substantial efficacy in enhancing recommender systems’ performance. Nevertheless, several recommender systems directly utilize original feature information as a foundational element for decision-making, which seems simplistic and low efficient. Furthermore, the incorporation of sequential decision-making adds complexity to the task of recommendation. In pursuit of maximizing the long-term sequential returns of recommender systems, our study introduces a novel architecture, named User Information Separating Architecture (UISA). This framework is tailored to align with classic reinforcement learning algorithms and aims to extract the user’s interest value through the discrete processing of both static and dynamic user information. Through integration with deep reinforcement learning, the architecture is oriented towards the maximization of long-term profit and is applicable in sequential recommendation scenarios. We conduct experimental assessments by combining the proposed architecture with proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG) algorithms. The outcomes illustrate marked improvements in commodity recommendation, showcasing enhancements ranging from approximately 5% to 40% in both reward and click-through rate metrics across a self-constructed JDEnv environment and the Virtual Taobao environment. Through comparison experiments, the UISA models demonstrate comparable performance.

Containment control of systems with constraints and bounded disturbances

AbstractThis article studies the distributed containment control of a second‐order discrete‐time multi‐agent system with convex constraints on positions, nonconvex constraints on velocities, bounded disturbances on static points and switching topologies. A distributed control algorithm with disturbance information and local information is proposed. It is proved that when the static points with disturbances and the joint graph have a directed spanning forest among each time interval, all agents are gradually driven to the neighborhood of a convex hull spanned by the static points, and an upper bound of the neighborhood is obtained. The most innovative point is to utilize a rigorous mathematical theory to deal with convex constraint error rather than a geometric explanation. Meanwhile, another control algorithm with static point information and local information is proposed to converge all agents to the convex hull in the absence of external disturbances. Additionally, a numerical example is conducted to illustrate this theory.

A declarative approach to detecting design patterns from Java execution traces and source code

Design patterns are invaluable for software engineers because they help obtain well-structured and reusable object-oriented software components and contribute towards ease of software comprehension, maintenance, and modification. However, identifying design patterns from an inspection of the source code is not easy because, in most cases, there are no syntactic cues that signal their presence. This topic has therefore elicited considerable interest in the field. The novel aspect of our work is that we propose a set of primitives using which we can declaratively specify design patterns based on a combination of static and dynamic information. Our experimental work is carried out in the context of Java: static information is extracted from Java source code, and dynamic information from an execution trace of a program. Each declarative pattern specification is automatically translated into an SQL query which retrieves all instances of the design pattern present in the program. We illustrate our approach with the well-known Gang-of-Four design patterns and the approach extends to other such design patterns. The experimental results show the efficacy of our approach for representative programs for all GoF patterns in addition to more extensive case studies, JHotDraw, Junit, and QuickUML.