CSM-H-R: An Automatic Context Reasoning Framework for Interoperable Intelligent Systems and Privacy Protection

Automation of High-Level Context (HLC) reasoning for intelligent systems at scale is imperative due to the unceasing accumulation of contextual data in the IoT era, the trend of the fusion of data from multi-sources, and the intrinsic complexity and dynamism of the context-based decision-making process. To mitigate this issue, we propose an automatic context reasoning framework CSM-H-R, which programmatically combines ontologies and states at runtime and the model-storage phase for attaining the ability to recognize meaningful HLC, and the resulting data representation can be applied to different reasoning techniques. Case studies are developed based on an intelligent elevator system in a smart campus setting. An implementation of the framework - a CSM Engine, and the experiments of translating the HLC reasoning into vector and matrix computing especially take care of the dynamic aspects of context and present the potentiality of using advanced mathematical and probabilistic models to achieve the next level of automation in integrating intelligent systems; meanwhile, privacy protection support is achieved by anonymization through label embedding and reducing information correlation. The code of this study is available at: https://github.com/songhui01/CSM-H-R.Comment: 11 pages, 8 figures, Keywords: Context Reasoning, Automation, Intelligent Systems, Context Modeling, Context Dynamism, Privacy Protection, Context Sharing, Interoperability, System Integratio

Automatic Controllable Colorization via Imagination

We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image, we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring, mimicking the process of human experts. As the synthesized images can be imperfect or different from the original grayscale image, we propose a Reference Refinement Module to select the optimal reference composition. Unlike most previous end-to-end automatic colorization algorithms, our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples. Extensive experiments demonstrate the superiority of our framework over existing automatic colorization algorithms in editability and flexibility. Project page: https://xy-cong.github.io/imagine-colorization.Comment: CVPR 2024. Project page: https://xy-cong.github.io/imagine-colorizatio

Indium Phosphide Bismide

Indium phosphide bismide is a new member to the dilute bismide family. Since the first synthesis by molecular beam epitaxy (MBE) in 2013, it has cut a figure for its abnormal properties comparing with other dilute bismides. Bismuth (Bi) incorporation is always a difficulty for epitaxial growth of dilute. In this chapter, it shows how to regulate MBE growth parameters and their influence on Bi incorporation in InP1−xBix. Structural, electronic and optical properties are systematically reviewed. Thermal annealing to study Bi thermal stability and its effect on physical properties is performed. InP1−xBix shows strong and broad photoluminescence at room temperature, which is a potential candidate for fabricating super-luminescence diodes applied for enhancing spatial resolution in optical coherence tomography. Quaternary phosphide bismide, including InGaPBi and InAlPBi, is briefly introduced in this chapter

Speech Separation Algorithm Using Gated Recurrent Network Based on Microphone Array

AbstractSpeech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position information. Since the GRU network has the advantage of processing time series data with faster training speed and fewer training parameters, the GRU model is adopted to process the separation features of several sequential frames in the same sub-band to estimate the ideal Ratio Masking (IRM). The proposed algorithm decomposes the mixture signals into time-frequency (TF) units using gammatone filter bank in the frequency domain, and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM. The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost. Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech separation in noisy and reverberant environments, provides good performance in terms of speech quality and intelligibility, and has the generalization capacity to reverberate. Abstract Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position information. Since the GRU network has the advantage of processing time series data with faster training speed and fewer training parameters, the GRU model is adopted to process the separation features of several sequential frames in the same sub-band to estimate the ideal Ratio Masking (IRM). The proposed algorithm decomposes the mixture signals into time-frequency (TF) units using gammatone filter bank in the frequency domain, and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM. The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost. Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech separation in noisy and reverberant environments, provides good performance in terms of speech quality and intelligibility, and has the generalization capacity to reverberate

Validity of self-reported weight, height and resultant body mass index in Chinese adolescents and factors associated with errors in self-reports

<p>Abstract</p> <p>Background</p> <p>Validity of self-reported height and weight has not been adequately evaluated in diverse adolescent populations. In fact there are no reported validity studies conducted in Asian children and adolescents. This study aims to examine the accuracy of self-reported weight, height, and resultant BMI values in Chinese adolescents, and of the adolescents' subsequent classification into overweight categories.</p> <p>Methods</p> <p>Weight and height were self-reported and measured in 1761 adolescents aged 12-16 years in a cross-sectional survey in Xi'an city, China. BMI was calculated from both reported values and measured values. Bland-Altman plots with 95% limits of agreement, Pearson's correlation and Kappa statistics were calculated to assess the agreement.</p> <p>Results</p> <p>The 95% limits of agreement were -11.16 and 6.46 kg for weight, -4.73 and 7.45 cm for height, and -4.93 and 2.47 kg/m²for BMI. Pearson correlation between measured and self-reported values was 0.912 for weight, 0.935 for height and 0.809 for BMI. Weighted Kappa was 0.859 for weight, 0.906 for height and 0.754 for BMI. Sensitivity for detecting overweight (includes obese) in adolescents was 56.1%, and specificity was 98.6%. Subjects' area of residence, age and BMI were significant factors associated with the errors in self-reporting weight, height and relative BMI.</p> <p>Conclusions</p> <p>Reported weight and height does not have an acceptable agreement with measured data. Therefore, we do not recommend the application of self-reported weight and height to screen for overweight adolescents in China. Alternatively, self-reported data could be considered for use, with caution, in surveillance systems and epidemiology studies.</p

Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.Comment: Accepted by ICDE 202

Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory

Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and reasoning of history to generate high-quality responses. However, such repeated recall-reason steps easily produce biased thoughts, \textit{i.e.}, inconsistent reasoning results when recalling the same history for different questions. On the contrary, humans can keep thoughts in the memory and recall them without repeated reasoning. Motivated by this human capability, we propose a novel memory mechanism called TiM (Think-in-Memory) that enables LLMs to maintain an evolved memory for storing historical thoughts along the conversation stream. The TiM framework consists of two crucial stages: (1) before generating a response, a LLM agent recalls relevant thoughts from memory, and (2) after generating a response, the LLM agent post-thinks and incorporates both historical and new thoughts to update the memory. Thus, TiM can eliminate the issue of repeated reasoning by saving the post-thinking thoughts as the history. Besides, we formulate the basic principles to organize the thoughts in memory based on the well-established operations, (\textit{i.e.}, insert, forget, and merge operations), allowing for dynamic updates and evolution of the thoughts. Furthermore, we introduce Locality-Sensitive Hashing into TiM to achieve efficient retrieval for the long-term conversations. We conduct qualitative and quantitative experiments on real-world and simulated dialogues covering a wide range of topics, demonstrating that equipping existing LLMs with TiM significantly enhances their performance in generating responses for long-term interactions

Vascular endothelial growth factor levels in diabetic peripheral neuropathy: a systematic review and meta-analysis

ObjectiveVascular endothelial growth factors (VEGFs, including VEGF-A, VEGF-B, VEGF-C, VEGF-D and PLGF) have important roles in the development and function of the peripheral nervous system. Studies have confirmed that VEGFs, especially VEGF-A (so called VEGF) may be associated with the diabetic peripheral neuropathy (DPN) process. However, different studies have shown inconsistent levels of VEGFs in DPN patients. Therefore, we conducted this meta-analysis to evaluate the relationship between cycling levels of VEGFs and DPN.MethodsThis study searched 7 databases, including PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, WanFang Database, and Chinese Biomedical Literature (CBM), to find the target researches. The random effects model was used to calculate the overall effect.Results14 studies with 1983 participants were included, among which 13 studies were about VEGF and 1 was VEGF-B, so only the effects of VEGF were pooled. The result showed that there were obviously increased VEGF levels in DPN patients compared with diabetic patients without DPN (SMD:2.12[1.34, 2.90], p&lt;0.00001) and healthy people (SMD:3.50[2.24, 4.75], p&lt;0.00001). In addition, increased circulating VEGF levels were not associated with an increased risk of DPN (OR:1.02[0.99, 1.05], p&lt;0.00001).ConclusionCompared with healthy people and diabetic patients without DPN, VEGF content in the peripheral blood of DPN patients is increased, but current evidence does not support the correlation between VEGF levels and the risk of DPN. This suggests that VEGF may play a role in the pathogenesis and repairment of DPN

Kinetics of non-structural protein 1, IgM and IgG antibodies in dengue type 1 primary infection

<p>Abstract</p> <p>Background</p> <p>Early and accurate diagnosis of dengue infection is essential for control of disease outbreaks. Recently, the dengue virus non-structural antigen 1 (NS1), a conserved and secreted glycoprotein, has been used as a marker for early diagnosis of dengue with convenience and cost-effectiveness. Serological tests of dengue IgM and IgG antibodies are still the most widely used for diagnosis of dengue. In order to assess combined diagnostic value of these tests, we study the kinetic profiles of circulating NS1, dengue IgM and IgG antibodies over the course of the disease by using an in-house dengue type 1 (DENV1) specific NS1 capture ELISA and the commercial Panbio Dengue IgM and IgG capture ELISAs.</p> <p>Results</p> <p>A panel of 313 acute-and early convalescent-phase serum specimens from 140 DENV1 primary infected patients during an outbreak of dengue in Guangzhou, China, in 2006 were studied. Dengue NS1 presented high levels in acute-phase serum samples. It was detectable as early as day 1 of illness, and up to 14 day after onset. The sensitivity of NS1 detection was ranged from 81.8% to 91.1% with samples taken during the first 7 days. Anti-dengue IgM antibody was detectable on the third day of onset with the positive rate of 42.9%, and rapidly increasing to 100% by day 8 of illness. Anti-dengue IgG antibody was detectable on the fifth day of onset with low level at the first week of onset, and slowly increasing to 100% by day 15 of illness. Combining the results of NS1 and IgM antibody detection allowed positive diagnosis in 96.9% -100% for samples taken after day 3 of onset.</p> <p>Conclusions</p> <p>Dengue NS1 detection might shorten the window period by first few days of illness. A combination of dengue NS1 antigen and IgM antibody testing facilitates enhanced diagnosis rates. The procedures should be suitable for developing countries where dengue is endemic.</p