Information Rate Research Articles

Sum Rate Optimization for MIMO Multicasting Network with Active IRS

This paper considers a multiple-input multiple-output (MIMO) multicasting system aided by the intelligent reflecting surface (IRS). We aim to maximize the sum information rate via jointly designing the transmit precoding matrix and the reflecting coefficient (RC) matrix, subject to the transmit power constrains of the Tx and IRS. To tackle the nonconvex problem, we recast the original problem into an equivalent formulation by using some important facts about matrices and proposed a block coordinate descent (BCD) method to optimize the variables. Finally, simulation results validate the effectiveness of active IRS in enhancing the rate performance.

A frequency domain multiplexing scheme based on kernel density estimation for neural communication systems

Transmitting information in engineered neural communication systems is a promising solution to delay-sensitive applications for the Internet of Bio-Nanothings (IoBNTs). As widely used in wired and wireless communication systems, introducing multiplexing into neural communication system could improve channel transmission efficiency. In this paper, we model a neural communication system for IoBNTs and propose a neural signal multiplexing scheme for this system, based on frequency-division multiplexing (FDM) principles. The whole system including channel modeling, neural encoding, demultiplexing scheme, and decoding method using kernel density estimation (KDE) are presented. The optimal parameters for KDE and bit error probability are analyzed, and the performance of the proposed strategy is evaluated in terms of error rate and mutual information rate. The work can help researchers better understanding the underlying mechanism of neural multiplexing and pave the way for the implementation of IoBNT applications.

On information theoretical modelling of the communications between neurons and the corresponding information rate

AbstractIn a nervous system propagation of spikes among neurons can be interpreted as an information transmission process, where, a neural response carries information about the stimulus. Quantification of this information might be of theoretical and practical importance. In this paper, (i) The interaction between two neurons with an electrically coupled transport of ions is modelled mathematically, where the communication through the junction gap between very close neurons occurs with a specific time delay; (ii) then, the connection structure of neurons is proposed based on a particular Rulkov network model. The corresponding process is modelled as a non‐linear binary channel with memory; and (iii) by assuming a recurrence relation for number of available membrane potential changing due to the arrival of the spike to the axonal terminal, the mutual information between channel input and output is obtained and the input range maximizing mutual information, and hence, the information rate of channel is derived.

Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization

Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for rating predictions, which has not been investigated before. We exploit the inherent characteristics of MMMF to assess the confidence level of algorithm’s prediction of individual rating and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm’s predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches.

Systematic Integration of Multi-Informant Externalizing Ratings in Clinical Settings.

Best practice clinical assessment of externalizing problems often necessitates collection of information from parents, youth themselves, and teachers. The present study tested the predictive validity of a psychometrically-driven scoring procedure to integrate multi-informant, dimensional ratings of externalizing problems. Participants were 2264 clinic-referred youth ages 6-18. Parents, teachers, and youth completed questionnaire ratings of externalizing problems (hyperactivity-inattention, conduct problems, and oppositionality-defiance) prior to an initial clinical appointment. The predictive validity of simple (highest informant rating; and all informant ratings separately) and more complex (latent S-1 bifactor model with specific informant factors; and moderated nonlinear factor analysis accounting for child age and sex) methods of informant integration was tested in predicting impairment, comorbidity, and number of clinical encounters. A simple model, in which all informant ratings were included, showed the best predictive validity across outcomes, performing as well or better than the use of the highest informant ratings or more complex latent variable models. The addition of child age and sex as moderators in the factor model did not improve predictive validity. Each informant (parent, teacher, and youth) contributes important information to the prediction of clinically-relevant outcomes. There is insufficient evidence at present to suggest that complex latent variable models should be favored over simpler models that preserve each informant's ratings.

Optimal Broadcast Scheduling Algorithm for a Multi-AUV Acoustic Communication Network

In systems of multiple autonomous underwater vehicles (AUVs), to achieve cooperative operation and cluster intelligence, information is often disseminated via broadcasting. However, due to the long propagation delay and slow transmission rate of underwater acoustic communication, traditional broadcast scheduling algorithms require a long broadcast period to avoid signal collision. To improve the channel utilization rate as much as possible and improve the update rate for broadcast information, we propose an optimal broadcast scheduling algorithm. This algorithm uses the location information of AUVs to adjust the broadcast sequence and broadcast schedule, to achieve the shortest possible collision-free broadcast period in the broadcast network for the current node distribution. Simulation experiments show that this algorithm can achieve a broadcast period much shorter than that of traditional TDMA and higher channel utilization without signal collision. In addition, the simulations prove the feasibility of applying the algorithm in an actual MAC protocol.

Probabilistic Matrix Factorization Recommendation Approach for Integrating Multiple Information Sources

Most previous studies on matrix factorization (MF)-based collaborative filtering (CF) have focused solely on user rating information for predicting recommendations. However, to further enhance the performance of recommender systems (RSs), it is important to also consider review information and rating reliability in the model. This article proposes a new probabilistic MF (PMF)-based CF method that integrates multiple information sources to provide reliable predictions. First, we introduce a sentiment-based PMF to handle user reviews and fit the normalized sentiment information obtained from our previously proposed sentiment analysis method. We also consider the helpfulness of reviews to highlight their reliability and effectiveness in this model. Subsequently, our proposed noise detection method is adopted to determine the reliability of user ratings, and then the rating matrix is transformed into a binary reliability matrix. A rating reliability-based PMF through Bernoulli distribution is then proposed to factorize it. To effectively integrate three types of information (ratings, reviews, and rating reliability) into a PMF procedure, we design a weight matrix using the proposed weighting strategy to generate a set of comprehensive prediction ratings with corresponding reliability probabilities. Experiments on four Amazon datasets demonstrate that our model outperforms comparison methods in terms of comprehensive evaluation.

Robust Doppler compensation for low frequency underwater communications

Impairment caused by Doppler frequency shift and harsh channel condition distinguish underwater acoustic communications from radio communications. Slow speed of acoustic waves produces relatively high Doppler shift and result in expansion or compression in time samples, which is known as Doppler scaling. In particular, the Doppler scaling causes significant carrier frequency offset (CFO) in orthogonal frequency division modulation (OFDM). To compensate the CFO, it is essential to resample signal samples according to the Doppler scale. In theory, the Doppler scale can be estimated by measuring the distance between peaks of the matched filter output of consecutive Doppler-invariant preambles. In practice, multi-path reflections and non-orthogonality between the HFM and OFDM payload produce large numbers of local false peaks. In this paper, we propose a robust Doppler estimation technique by employing constant false alarm rate (CFAR) filter to remove false peaks. We validate the performance of the proposed scheme through two sea-trials carried out in shallow water near Jervis Bay, Australia. In the sea-trials, a slow-moving towed hydrophone array was deployed as the receiver and the source was a stationary, low source level, vertical transmit array. Sea-trial results demonstrated up to 112bps information rate using bit-interleaved coded OFDM over 400 Hz bandwidth.

Improper Gaussian Signaling for IRS Assisted Multiuser SWIPT Systems With Hardware Impairments

In this paper, we focus on intelligent reflecting surface (IRS) assisted multiuser simultaneous wireless information and power transfer (SWIPT) systems with hardware impairments (HWIs), which result from the aggregate effect of in-phase and quadrature-phase imbalance (IQI), power amplifier non-linearity, quantization distortion, phase noise, etc. Improper Gaussian signaling (IGS) is employed to combat the signal distortion incurred by HWIs and the interference from other users. We aim to maximize the minimum achievable information rate among all users by jointly optimizing the active beamforming vectors, passive reflecting coefficients, and power splitting coefficients, subject to the minimum harvested energy requirements of these users and the total power budget at the transmitter. Due to the intricate coupling between the reflecting coefficients and beamforming vectors, we propose a two-level iterative optimization algorithm to solve this non-convex problem. Based on the above, we also study the weighted rate-energy region maximization problem. Numerical results show that the use of IGS significantly outperforms traditional proper Gaussian signaling (PGS) in the considered systems and also affirm the effectiveness of the proposed optimization algorithm.

Unmanned-Aerial-Vehicle-Aided Integrated Sensing and Computation With Mobile-Edge Computing

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this paper, we investigate the unmanned aerial vehicle (UAV) aided ISAC with mobile edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a system-wise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict non-convexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.

Cortical circuits modulate mouse social vocalizations.

Vocalizations provide a means of communication with high fidelity and information rate for many species. Diencephalon and brainstem neural circuits have been shown to control mouse vocal production; however, the role of cortical circuits in this process is debatable. Using electrical and optogenetic stimulation, we identified a cortical region in the anterior cingulate cortex in which stimulation elicits ultrasonic vocalizations. Moreover, fiber photometry showed an increase in Ca2+ dynamics preceding vocal initiation, whereas optogenetic suppression in this cortical area caused mice to emit fewer vocalizations. Last, electrophysiological recordings indicated a differential increase in neural activity in response to female social exposure dependent on vocal output. Together, these results indicate that the cortex is a key node in the neuronal circuits controlling vocal behavior in mice.

Factors in Vaccine Refusal by Patients Applying for COVID-19 PCR Test.

This study was designed to determine the reasons for vaccine rejection in patients who applied for the coronavirus disease-2019 (COVID-19) polymerase chain reaction (PCR) test but did not receive the vaccine. The study was conducted prospectively in the emergency department of a tertiary hospital between 31.01.2022 and 31.05.2022. 1000 patients who applied for the COVID-19 PCR test and refused to be vaccinated were included. The COVID-19 status of the participants, reasons for their application, number of PCR tests, methods of obtaining information about the vaccine, and reasons for the rejection of the vaccine were questioned. 54.6% of the participants were male and 45.4% were female. 60.7% of the patients applied for testing due to symptoms, 25.4% due to contact with people with symptoms, and 23.9% due to travel. 43.3% of the cases had COVID-19 infection; 53.6% of them had tested an average of 2-5 times in the last year. Most of the information about the vaccine was taken from social media, television, medical publications, and people around, respectively. Of the participants, 62.0% believed that COVID-19 vaccines had side effects, 47.3% believed that it had no protection, and 30.9% believed that there was not enough study on the subject. As the age grew, the rate of learning information from social media increased. The most common reasons for COVID-19 vaccine rejection were vaccine side effects, doubtful vaccine protection, and concerns about the lack of sufficient studies on the vaccine. The higher the education level, the higher the vaccine rejection rate.

Information Leakage Rate of Optical Code Division Multiple Access Network Using Wiretap Code.

Secrecy capacity is usually employed as the performance metric of the physical layer security in fiber-optic wiretap channels. However, secrecy capacity can only qualitatively evaluate the physical layer security, and it cannot quantitatively evaluate the physical layer security of an imperfect security system. Furthermore, secrecy capacity cannot quantitatively evaluate the amount of information leakage to the eavesdropper. Based on the channel model of an optical CDMA network using wiretap code, the information leakage rate is analyzed to evaluate the physical layer security. The numerical results show that the information leakage rate can quantitatively evaluate the physical layer security of an optical CDMA wiretap channel, and it is related to transmission distance, eavesdropping position, confidential information rate and optical code.

Short-term safety of inactivated SARS-Cov-2 vaccines in Chinese patients with central nervous system inflammatory demyelinating diseases

ObjectiveThis study aims to evaluate the short-term safety of inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in Chinese patients with central nervous system inflammatory demyelinating diseases (CNS IDDs). MethodsA web-based survey was conducted among patients with CNS IDDs from April 15 to 19, 2022 in China. In total, 645 patients with CNS IDDs were identified, including 425 patients with multiple sclerosis (MS), 194 with neuromyelitis optica spectrum disorder (NMOSD), and 26 with other CNS IDDs. The questionnaire consisted of demographic data, clinical records, history of SARS-CoV-2 vaccination, and vaccination-related symptoms within one month after vaccination. The demographic data, clinical information, and relapse rates between vaccinated and non-vaccinated patients were compared. ResultsAmong 645 patients with CNS IDDs, 78 were vaccinated and 567 were non-vaccinated with the vaccination rate of 12.1 %. Compared to non-vaccinated group, a lower percentage of patients on DMDs therapy (41.0 % vs. 71.8 %, P < 0.001) and an increased proportion of patients with other vaccination in past 3 years (17.9 % vs. 4.8 %, P < 0.001) were observed in vaccinated group. Six patients experienced a relapse within 30 days of a vaccination. Additionally, vaccine-associated relapse rates in vaccinated patients did not significantly differ from these in non-vaccinated patients among 2020, 2021, and from January 1 to October 1, 2022. ConclusionsNo increased risk of vaccination-associated relapses among Chinese patients with CNS IDDs indicated that inactivated SARS-CoV-2 vaccines appear to be safe for this population.

Media sentiment, news and liquidity of Chinese property developer stocks amidst the shadow of a mortgage crisis in China

PurposeThe study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.Design/methodology/approachThe authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.FindingsThe authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.Originality/valueThe findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

The hybrid pulse amplitude width modulation scheme: high efficiency technique for dimmable VLC systems

Abstract Visible-light communications (VLC) is an environmentally friendly optical wireless communication (OWC) technology that operates in the 400–800 THz frequency band. It is currently a critical technology for 5G and 6G wireless indoor applications due to its high data rates, low latency, and superior reliability. Intensity modulated direct detection (IM/DD) is widely used in VLC systems. A channel modulation scheme is designed using L-level pulse modulation techniques such as on–off keying (OOK), amplitude modulation (PAM), position modulation (PPM), and the width modulation (PWM). We introduce a novel hybrid modulation scheme n-M-PAWM based on both PAM and PWM schemes to improve throughput and significantly increase power and spectrum efficiency in VLC communication systems. The performance of our proposed hybrid n-M-PAWM scheme is studied in several aspects, including the number of transmitted bits per symbol, average BER, information rate ratio (IRR), PAPR, bandwidth, and power requirement. In an extremely noisy VLC system, the proposed 2-M-PAWM modulation outperforms both PPWM and PAPM modulation schemes due to its better BER performances. Additionally, we examine the performance of the VLC system in terms of spectral efficiency (SE) using our proposed PAWM-based dimming control system by varying the number of pulses transmitted during one symbol period. We demonstrate through simulation results that our PAWM-based dimming control system performs significantly better compared to the other pulse modulation schemes. All results of this research show that our proposed hybrid PAWM modulation scheme has high potential for VLC technology in indoor and noisy environments.

Value Relevance of Accounting Information and Share Rate: A Study of Manufacturing Companies in Nigeria

This research work determined the impact of value relevance of accounting information on the stock price of manufacturing companies in Nigeria. More specifically, the study determines the impact of earnings per share and dividends on the stock price of Nigerian manufacturing companies. Ex post facto research and data were obtained from twenty audited annual reports and financial statements. This study used the ordinary least squares (OLS) estimation from 2012 to 2021 and covered ten years with the help of E-view 9.0 data analysis revealed that there is a positive insignificant relationship between stock dividends, earnings per share and stock price in the manufacturing sector in Nigeria.

Comments on “Channel Coding Rate in the Finite Blocklength Regime”: On the Quadratic Decaying Property of the Information Rate Function

The quadratic decaying property of the information rate function states that given a fixed conditional distribution $p_{\mathsf{Y}|\mathsf{X}}$, the mutual information between the (finite) discrete random variables $\mathsf{X}$ and $\mathsf{Y}$ decreases at least quadratically in the Euclidean distance as $p_\mathsf{X}$ moves away from the capacity-achieving input distributions. It is a property of the information rate function that is particularly useful in the study of higher order asymptotics and finite blocklength information theory, where it was already implicitly used by Strassen [1] and later, more explicitly, by Polyanskiy-Poor-Verd\'u [2]. However, the proofs outlined in both works contain gaps that are nontrivial to close. This comment provides an alternative, complete proof of this property.

Interfacial Shift Keying Allows a High Information Rate in Molecular Communication: Methods and Data

Interfacial Shift Keying Allows a High Information Rate in Molecular Communication: Methods and Data

Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation.

To assess the ability of an automated AI tool to detect intravenous contrast material (IVCM) in abdominal CT examinations using spleen attenuation. A previously validated automated AI tool measuring the attenuation of the spleen was deployed on a sample of 32,994 adult (age ≥ 18) patients (mean age, 61.9 ± 14.7 years; 13,869 men, 19,125 women) undergoing 65,449 supine position CT examinations (41,020 with and 24,429 without IVCM by DICOM header) from January 1, 2000 to December 31, 2021. After exclusions, receiver operating characteristic (ROC) curve analysis was performed to determine the optimal threshold for binary classification of IVCM status (non-contrast vs IVCM enhanced), which was then applied to the sample. Discordant examinations (i.e., IVCM status determined by AI tool did not match DICOM header) were manually reviewed to establish ground truth. Repeat ROC curve and contingency table analysis were performed to assess AI tool performance. ROC analysis of the initial study sample of 61,783 CT examinations yielded AUC of 0.970 with Youden index suggesting an optimal spleen attenuation threshold of 65 Hounsfield units (HU). Manual review of 2094 discordant CT examinations revealed discordance due to DICOM header error in 1278 (61.0%) and AI tool misclassification in 410 (19.6%), with 406 (9.4%) meeting exclusion criteria. Analysis of 61,377 CT examinations in the final study sample yielded AUC of 0.999 with accuracy of 99.3% at the 65 HU threshold. Error rate for DICOM header information was 2.1% (1278/61,377) versus 0.7% (410/61,377) for the AI tool. The automated spleen attenuation AI tool was highly accurate for detection of IVCM at a threshold of 65 HU.