Semantic Multi-Resolution Communications

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below $10^{-2}$.Comment: arXiv admin note: text overlap with arXiv:2310.0685

A Malignant Mass in the Breast Is Not Always Breast Cancer

A 37-year-old woman presented to the Internal Medicine Clinic with complaints of abdominal pain and constipation which had begun 3 months earlier. A colonoscopy was performed, and wall thickening of the sigmoid colon was detected. A biopsy of the sigmoid colon revealed a poorly differentiated, mucin-producing adenocarcinoma with a signet-ring pattern. No distant metastasis was detected. The patient was treated with chemotherapy consisting of 5-fluorouracil, leucovorin, and oxaliplatin. One and a half years later, a painless mass, which was not fixed to the skin, measuring 1 cm in diameter, was found in the lower outer quadrant of the left breast. A core biopsy of the mass was performed, and a histopathological report confirmed metastasis to the breast from mucinous adenocarcinoma of an intestinal primary

Reproducibility of endometrial intraepithelial neoplasia diagnosis is good, but influenced by the diagnostic style of pathologists

Endometrial intraepithelial neoplasia (EIN) applies specific diagnostic criteria to designate a monoclonal endometrial preinvasive glandular proliferation known from previous studies to confer a 45-fold increased risk for endometrial cancer. In this international study we estimate accuracy and precision of EIN diagnosis among 20 reviewing pathologists in different practice environments, and with differing levels of experience and training. Sixty-two endometrial biopsies diagnosed as benign, EIN, or adenocarcinoma by consensus of two expert subspecialty pathologists were used as a reference comparison to assess diagnostic accuracy of 20 reviewing pathologists. Interobserver reproducibility among the 20 reviewers provided a measure of diagnostic precision. Before evaluating cases, observers were self-trained by reviewing published textbook and/or online EIN diagnostic guidelines. Demographics of the reviewing pathologists, and their impressions regarding implementation of EIN terminology were recorded. Seventy-nine percent of the 20 reviewing pathologists' diagnoses were exactly concordant with the expert consensus (accuracy). The interobserver weighted kappa values of 3-class EIN scheme (benign, EIN, carcinoma) diagnoses between expert consensus and each of reviewing pathologists averaged 0.72 (reproducibility, or precision). Reviewing pathologists demonstrated one of three diagnostic styles, which varied in the repertoire of diagnoses commonly used, and their nonrandom response to potentially confounding diagnostic features such as endometrial polyp, altered differentiation, background hormonal effects, and technically poor preparations. EIN diagnostic strategies can be learned and implemented from standard teaching materials with a high degree of reproducibility, but is impacted by the personal diagnostic style of each pathologist in responding to potential diagnostic confounders

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distributed computing), and how to learn/train machine learning models from private data spread across multiple users (privacy in distributed (federated) learning). The article motivates each privacy setting, describes the problem formulation, summarizes breakthrough results in the history of each problem, and gives recent results and discusses some of the major ideas that emerged in each field. In addition, the cross-cutting techniques and interconnections between the three topics are discussed along with a set of open problems and challenges

Multiuser Detection for Advanced Communication Systems and Networks

Potential for improved performance through joint detection of multiuser signals, coupled with associated challenges in achieving this potential at affordable receiver complexity, has motivated significant amount of research to be carried out in the area of multiuser detection (MUD) in the past two decades. Much of the early research in this important area has been centered around systems employing code division multiple access (CDMA) promising capacity improvement in terms of the number of simultaneous users supported in the system. The optimum MUD complexity, which is exponential in the number of users, has inspired a considerable effort toward the development of low-complexity, suboptimal alternatives capable of resolving the detrimental effects of multiple-access interference. Interference cancellation strategies have received particular attention, due to their competitive performance at low complexity and simple modular structure. Their performances, however, are still far from the optimum maximum-likelihood (ML) performance. Iterative methods based on soft-decision cancellation have been shown to achieve near-ML performance. Since most practical communication systems use coding, iterative multiuser decoding of coded CDMA signals has received considerable research attention, and so has the topic of joint multiuser channel estimation and decoding