Adaptive resource assignment along with overload control for the GSM/EGPRS networks

Enhanced General Packet Radio Services (EGPRS) is one of the proposals for third-generation (3G) wireless services. EGPRS is also the evolutionary path for GSM and IS-136 standards towards their next-generation wireless systems. The 3G services are categorized into the background, conversational, interactive and streaming services. Therefore, GSM towards 3G is staged into two phases. The phase one of EGPRS to provide Internet access services is known as General Packet Radio Service (GPRS). The phase two of EGPRS to provide 3G services integrates with the Enhanced Data rates for the GSM Evolution (EDGE). To provide the various 3G services and to achieve more efficient utilization of the frequency spectrum, our work is to focus on, the evolution of the system capacity and performance for the GSM/EGPRS networks. Therefore, an Adaptive Resource Assignment along with Overload Control (ARAOC) algorithm has been developed while integrating adaptive channel allocation, call admission control, frequency hopping and new congestion control schemes. Our simulation results show that this algorithm can greatly improve the system capacity and performance as well as the QoS for users. The influence of the variable parameters of user data rates, channel buffer size, and channel assignment parameter to the system capacity and performance, will be investigated

The Relationship between Public Service Efficiency of Government and Residential Political Trust in Hong Kong

Hong Kong has a long history with its high efficiency and clean and self-disciplined government. Within the past over 20 years, different social development trend has occurred in Hong Kong. The article observed the relationship between political trust from residence and public service efficiency of government in Hong Kong from 1992 to 2015 and found that the value of public service efficiency has a significant effect on political trust in Hong Kong government, the higher the efficiency of public services, the higher the political trust. The author tried to find the path for the Hong Kong government to improve its public service quality and efficiency after testifying the positive correlation between public service efficiency and residential political trust with empirical analysis

RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now, automatic evaluation methods need better consistency with humans, while reliable human evaluation can be time- and cost-intensive. To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem. Specifically, RADE explicitly compares reference and the candidate response to predict their overall scores. Moreover, an auxiliary response generation task enhances prediction via a shared encoder. To support RADE, we extend three datasets with additional rated responses other than just a golden response by human annotation. Experiments on our three datasets and two existing benchmarks demonstrate the effectiveness of our method, where Pearson, Spearman, and Kendall correlations with human evaluation outperform state-of-the-art baselines.Comment: 19 pages, Accepted by ACL2023 main conferenc

Intraperitoneal injection of thalidomide attenuates bone cancer pain and decreases spinal tumor necrosis factor-α expression in a mouse model

<p>Abstract</p> <p>Background</p> <p>Tumor necrosis factor α (TNF-α) may have a pivotal role in the genesis of mechanical allodynia and thermal hyperalgesia during inflammatory and neuropathic pain. Thalidomide has been shown to selectively inhibit TNF-α production. Previous studies have suggested that thalidomide exerts anti-nociceptive effects in various pain models, but its effects on bone cancer pain have not previously been studied. Therefore, in the present study, we investigated the effect of thalidomide on bone cancer-induced hyperalgesia and up-regulated expression of spinal TNF-α in a mouse model.</p> <p>Results</p> <p>Osteosarcoma NCTC 2472 cells were implanted into the intramedullary space of the right femurs of C3H/HeJ mice to induce ongoing bone cancer related pain behaviors. At day 5, 7, 10 and 14 after operation, the expression of TNF-α in the spinal cord was higher in tumor-bearing mice compared to the sham mice. Intraperitoneal injection of thalidomide (50 mg/kg), started at day 1 after surgery and once daily thereafter until day 7, attenuated bone cancer-evoked mechanical allodynia and thermal hyperalgesia as well as the up-regulation of TNF-α in the spinal cord.</p> <p>Conclusions</p> <p>These results suggest that thalidomide can efficiently alleviate bone cancer pain and it may be a useful alternative or adjunct therapy for bone cancer pain. Our data also suggest a role of spinal TNF-α in the development of bone cancer pain.</p

Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries.Comment: 8 pages, 4 figure

Simulation and Analysis of the Thermal-Mechanical Response of an Energy Pile

AbstractAn energy pile undertakes the functions of supporting the superstructure and controlling the indoor temperature of the building, and the thermal-mechanical coupling response of an energy pile makes its load transfer mechanism different from that of conventional engineering piles. Moreover, the thermal-mechanical coupling responses of the energy piles in summer and winter conditions are also different and need to be explored separately. Based on a ground source heat pump pile foundation workshop project in Kunshan city, Jiangsu Province, a multiphysics simulation study was carried out. The simulation results of the outlet water temperature and pile settlement are consistent with the real-world measurements, which verifies the reliability of the numerical simulation. The responses of the temperature distribution, axial stress, lateral shear stress, and settlement of the energy pile in summer and winter were analyzed, and the response laws of the energy pile in different seasons were obtained. Compared with the pure conventional load state, under the effect of thermal-mechanical coupling in winter conditions, the maximum compressive stress of the pile body is reduced by about 11.5%, but the settlement of the pile top increases by about 47.66%. Therefore, the winter conditions should be used as the design energy for the normal use of the pile. The control condition of the limit state: compared with the pure conventional load state, the maximum compressive stress of the pile increases by about 12% and the settlement of the pile top decreases by about 7.23% under the thermal-mechanical coupling effect of the summer condition. Therefore, the summer condition is the pile control conditions for the limit state of the body’s carrying capacity

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently

Segment Anything Model (SAM) for Radiation Oncology

In this study, we evaluate the performance of the Segment Anything Model (SAM) model in clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \& neck, which are typical treatment sites in radiation oncology. For each case, we selected the OARs of concern in radiotherapy planning and compared the Dice and Jaccard outcomes between clinical manual delineation, automatic segmentation using SAM's "segment anything" mode, and automatic segmentation using SAM with box prompt. Our results indicate that SAM performs better in automatic segmentation for the prostate and lung regions, while its performance in the gastrointestinal and head \& neck regions was relatively inferior. When considering the size of the organ and the clarity of its boundary, SAM displays better performance for larger organs with clear boundaries, such as the lung and liver, and worse for smaller organs with unclear boundaries, like the parotid and cochlea. These findings align with the generally accepted variations in difficulty level associated with manual delineation of different organs at different sites in clinical radiotherapy. Given that SAM, a single trained model, could handle the delineation of OARs in four regions, these results also demonstrate SAM's robust generalization capabilities in automatic segmentation for radiotherapy, i.e., achieving delineation of different radiotherapy OARs using a generic automatic segmentation model. SAM's generalization capabilities across different regions make it technically feasible to develop a generic model for automatic segmentation in radiotherapy