Incomplete time-series modeling is an unavoidable topic in real-world time-series analysis because of the frequent occurrence of missing values in practical data. However, integrating data preprocessing and subsequent analysis within a model can amplify the errors from processed values. Moreover, most existing methods that directly model incomplete time series often fail to infer values at any desired time or support multistep prediction. To address these issues, this article introduces a novel generative model called the continuous latent adversarial autoencoder (CLAAE) for directly modeling incomplete time series. CLAAE can effectively impute missing data of any time point and support multistep prediction. Specifically, CLAAE devises a time-aware long short-term memory (LSTM) encoder to extract temporal and sequential characteristics. The decoder is built upon the augmented neural ordinary differential equation (ANODE), allowing it to infer the probability of missing data across an arbitrary continuous-time horizon. To guarantee the meaningfulness of samples generated from any region within the prior space, a fully connected neural network is utilized as a discriminator, encouraging the aggregated posterior learned by the encoder to be indistinguishable from a selected prior distribution. Extensive experimental results across simulations and real-world datasets demonstrate that CLAAE outperforms baseline methods, especially when the amount of missing data is overwhelming. By combining the autoencoder and adversarial training, CLAAE can significantly enhance the quality of the synthetic samples, respecting the original feature distributions and the temporal dynamics.

BACKGROUND: Two types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected. RESULTS: Given the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method. CONCLUSIONS: The proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH .

Composing Text and Image to Image Retrieval (CTI-IR) aims at finding the target image, which matches the query image visually along with the query text semantically. However, existing works ignore the fact that the reference text usually serves multiple functions, e.g., modification and auxiliary. To address this issue, we put forth a unified solution, namely Hierarchical Aggregation Transformer incorporated with Cross Relation Network (CRN). CRN unifies modification and relevance manner in a single framework. This configuration shows broader applicability, enabling us to model both modification and auxiliary text or their combination in triplet relationships simultaneously. Specifically, CRN includes: 1) Cross Relation Network comprehensively captures the relationships of various composed retrieval scenarios caused by two different query text types, allowing a unified retrieval model to designate adaptive combination strategies for flexible applicability; 2) Hierarchical Aggregation Transformer aggregates top-down features with Multi-layer Perceptron (MLP) to overcome the limitations of edge information loss in a window-based multi-stage Transformer. Extensive experiments demonstrate the superiority of the proposed CRN over all three fashion-domain datasets. Code is available at github.com/yan9qu/crn.

The Internet of Things (IoT) has created a ubiquitously connected world powered by a multitude of wired and wireless sensors generating a variety of heterogeneous data over time in a myriad of fields and applications. To extract complete information from these data, advanced artificial intelligence (AI) technology, especially deep learning (DL), has proved successful in facilitating data analytics, future prediction and decision making. The collective integration of AI and the IoT has greatly promoted the rapid development of AI-of-Things (AIoT) systems that analyze and respond to external stimuli more intelligently without involvement by humans. However, it is challenging or infeasible to process massive amounts of data in the cloud due to the destructive impact of the volume, velocity, and veracity of data and fatal transmission latency on networking infrastructures. These critical challenges can be adequately addressed by introducing edge computing. This article conducts an extensive survey of an end-edge-cloud orchestrated architecture for flexible AIoT systems. Specifically, it begins with articulating fundamental concepts including the IoT, AI and edge computing. Guided by these concepts, it explores the general AIoT architecture, presents a practical AIoT example to illustrate how AI can be applied in real-world applications and summarizes promising AIoT applications. Then, the emerging technologies for AI models regarding inference and training at the edge of the network are reviewed. Finally, the open challenges and future directions in this promising area are outlined.

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

This paper presents a linear Minimum Mean Square Error (MMSE) MIMO Detector design for MIMO-OFDM systems based on Application-Specific Instrument- set Processor (ASIP). As part of the IEEE 802.11ac-compliant PHY baseband transceiver, the proposed MIMO detector offers low latency, high throughput with efficient resource utilization. The design has been synthesized with TSMC 40 nm CMOS technology, the logic gate count for each QRD engine is about 245 K gates. It is able to support 20/40/80MHz bandwidth and up to 4 spatial streams. Detection latency for 80 MHz VHT mode (234 data sub-carriers) is 750 ns.

This paper addresses the issue of encoding of LDPC codes for certain transmission standards that employ LDPC as coding scheme. It is shown that for these LDPC codes, the encoding can be done simply by reusing decoding resources with negligible overhead. Compared to conventional encoding designs, our approach requires much less resources specified for encoder.

A low complexity M-algorithm based multiple-input multiple-output (MIMO) tree search algorithm with near maximum likelihood (ML) performance is proposed in this paper. Numerical examples show that our tree search algorithm is able to provide a significant performance gain over the MMSE detection. Based on this algorithm, a fully pipelined architecture is presented for the MIMO orthogonal frequency division multiplexing (OFDM) systems. The throughput for a 4x4 MIMO-OFDM IEEE 802.11n system with 64-QAM is 312 Mbps.

A pair of FBG embedded symmetrically in an elastic bending beam, resulting the two peak wavelengths moving apart with the downwards-applied lateral force and moving together in the same direction when surrounding temperature changes, both linearly. Proper calibration makes such arc-shaped FBG sensor capable of simultaneous measurement of both temperature and lateral force. A module with such structure is studied both theoretically and experimentally.

This paper presents the design of a (n,k) Reed-Solomon decoder for both errors and erasures. The key-equation solver is based on Sarwate's reformulated inversionless Berlekamp-Massey algorithm. The decoder has been implemented on FPGA and the maximum clock frequency can be 150 MHz for a (255, 239) code on a Xilinx Virtex-II device.

The authors report on the development and evaluation of a novel patient-centered technology that promotes capture of critical information necessary to drive guideline-based care for pediatric asthma. The design of this application, the asthma kiosk, addresses five critical issues for patient-centered technology that promotes guideline-based care: (1) a front-end mechanism for patient-driven data capture, (2) neutrality regarding patients' medical expertise and technical backgrounds, (3) granular capture of medication data directly from the patient, (4) formal algorithms linking patient-level semantics and asthma guidelines, and (5) output to both patients and clinical providers regarding best practice. The formative evaluation of the asthma kiosk demonstrates its ability to capture patient-specific data during real-time care in the emergency department (ED) with a mean completion time of 11 minutes. The asthma kiosk successfully links parents' data to guideline recommendations and identifies data critical to health improvements for asthmatic children that otherwise remains undocumented during ED-based care.