Information-theoretic Approach Research Articles

What dictates alkane isomerization? A combined density functional theory and information-theoretic approach study

What dictates alkane isomerization? A combined density functional theory and information-theoretic approach study

Application of Polling Scheduling in Mobile Edge Computing

With the Internet of Things (IoT) development, there is an increasing demand for multi-service scheduling for Mobile Edge Computing (MEC). We propose using polling for scheduling in edge computing to accommodate multi-service scheduling methods better. Given the complexity of asymmetric polling systems, we have used an information-theoretic approach to analyse the model. Firstly, we propose an asymmetric two-level scheduling approach with priority based on a polling scheduling approach. Secondly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability-generating function. By solving for the probability-generating function’s first-order partial and second-order partial derivatives, we calculate the exact expressions of the average queue length, the average polling period, and the average delay with an approximate analysis of periodic query way. Finally, we design a simulation experiment to verify that our derived parameters are correct. Our proposed model can better differentiate priorities in MEC scheduling and meet the needs of IoT multi-service scheduling.

Surprise-based JND estimation for perceptual quantization in H.265/HEVC codecs

Just noticeable distortion (JND), reflecting the perceptual redundancy directly, has been widely used in image and video compression. However, the human visual system (HVS) is extremely complex and the visual signal processing has not been fully understood, which result in existing JND models are not accurate enough and the bitrate saving of JND-based perceptual compression schemes is limited. This paper presents a novel pixel-based JND model for videos and a JND-based perceptual quantization scheme for HEVC codecs. In particular, positive and negative perception effects of the inter-frame difference and the motion information are analyzed and measured with an information-theoretic approach. Then, a surprise-based JND model is developed for perceptual video coding (PVC). In our PVC scheme, the frame-level perceptual quantization parameter (QP) is derived on the premise that the coding distortion is infinitely close to the estimated JND threshold. On the basis of the frame-level perceptual QP, we determine the perceptual QP for each coding unit through a perceptual adjustment function to achieve better perceptual quality. Experimental results indicate that the proposed JND model outperforms existing models significantly, the proposed perceptual quantization scheme improves video compression efficiency with better perceptual quality and lower coding complexity.

Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking.

In informational masking paradigms, the successful segregation between the target and masker creates auditory perceptual awareness. The dynamics of the build-up of auditory perception is based on a set of interactions between bottom-up and top-down processes that generate neuronal modifications within the brain network activity. These neural changes are studied here using event-related potentials (ERPs), entropy, and integrated information, leading to several measures applied to electroencephalogram signals. The main findings show that the auditory perceptual awareness stimulated functional activation in the fronto-temporo-parietal brain network through (i) negative temporal and positive centro-parietal ERP components; (ii) an enhanced processing of multi-information in the temporal cortex; and (iii) an increase in informational content in the fronto-central cortex. These different results provide information-based experimental evidence about the functional activation of the fronto-temporo-parietal brain network during auditory perceptual awareness.

Characterization of Low-Latency Next-Generation eVTOL Communications: From Channel Modeling to Performance Evaluation

Next-generation wireless communication networks are expected to offer extremely high data rates supported by very low latency and radically new applications, which require a new wireless radio technology paradigm. However, it is crucial to assist the radio link over the fast varying and highly dynamic channel to satisfy the diverse requirements of next-generation wireless networks. Recently, communication via autonomous electric vertical takeoff and landing (eVTOL) has gained momentum, owing to its potential for cost-effective network deployment. It is considered one of the most promising technologies conceived to support smart radio terminals. However, to provide efficient and reliable communications between ground base stations and eVTOLs as well as between eVTOLs and other eVTOLs, realistic eVTOL channel models are indispensable. In this paper, we propose a nonstationary geometry-based stochastic channel model for eVTOL communication links. The proposed eVTOL channel model framework considers time-domain nonstationarity and arbitrary eVTOL trajectory and is sufficiently general to support versatile C bands. One of the critical challenges for eVTOL is the fast vertical takeoff and landing flight patterns affecting the regular propagation communication channel. Moreover, we present a new method for estimating the SNR over the non-stationary fast dynamic time-variant eVTOL channel by utilizing the sliding window adaptive filtering technique. Furthermore, we present an information–theoretic approach to characterize the end-to-end transmission delay over the eVTOL channel and prove that the optimal transmission scheme strongly depends upon the eVTOL link configuration. In addition, to analyze the occurrence of deep fade regions in eVTOL links, we analyze the outage probability, which is an important performance metric for wireless channels operating over dynamic fading channels, and make an important observation that the outage probability increases non-linearly with the eVTOL height. Furthermore, we consider the commercially available eVTOL specifications and data to validate the channel model and analyze the Doppler shift and latency for the exponential acceleration and exponential deceleration velocities profiles during the takeoff and landing operation. This paper provides a new and practical approach for the design, optimization, and performance evaluation of future eVTOL-assisted next-generation wireless communications.

An information-theoretic approach to basis-set fitting of electron densities and other non-negative functions.

The numerical ill-conditioning associated with approximating an electron density with a convex sum of Gaussian or Slater-type functions is overcome by using the (extended) Kullback-Leibler divergence to measure the deviation between the target and approximate density. The optimized densities are non-negative and normalized, and they are accurate enough to be used in applications related to molecular similarity, the topology of the electron density, and numerical molecular integration. This robust, efficient, and general approach can be used to fit any non-negative normalized functions (e.g., the kinetic energy density and molecular electron density) to a convex sum of non-negative basis functions. We present a fixed-point iteration method for optimizing the Kullback-Leibler divergence and compare it to conventional gradient-based optimization methods. These algorithms are released through the free and open-source BFit package, which also includes a L2-norm squared optimization routine applicable to any square-integrable scalar function.

Hybrid Reflection Modulation

Reconfigurable intelligent surface (RIS)-empowered communication has emerged as a novel concept for customizing future wireless environments in a cost- and energy-efficient way. However, due to double path loss, existing fully passive RIS systems that purely reflect the incident signals into preferred directions attain an unsatisfactory performance improvement over the traditional wireless networks in certain conditions. To overcome this bottleneck, we propose a novel transmission scheme, named hybrid reflection modulation (HRM), exploiting both active and passive reflecting elements at the RIS and their combinations, which enables to convey information without using any radio frequency (RF) chains. In the HRM scheme, the active reflecting elements using additional power amplifiers are able to amplify and reflect the incoming signal, while the remaining passive elements can simply reflect the signals with appropriate phase shifts. Based on this novel transmission model, we obtain an upper bound for the average bit error probability (ABEP), and derive achievable rate of the system using an information theoretic approach. Moreover, comprehensive computer simulations are performed to prove the superiority of the proposed HRM scheme over existing fully passive, fully active and reflection modulation (RM) systems.

An information theoretic approach to detecting spatially varying genes

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.

Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging.

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.

The diachronic change of research article abstract difficulty across disciplines: a cognitive information-theoretic approach

Text difficulty refers to the ease with which a text can be read and understood, and the difficulty of research article abstracts has long been a hot topic. Previous studies have found that research article abstracts are difficult to read in general and that abstracts have gradually become more and more difficult. However, the widely used measurements, such as FRE and SMOG, have long been criticized in that they use only simplistic and surface-level indicators as proxies for complex cognitive processes of reading, and the sophisticated cognitive theory and Natural Language Processing/machine learning-based methods seem not that easy to use and interpret. A theoretically sound and methodologically neat measurement of text difficulty should be called for. Besides, the diachronic changes of abstract difficulty across disciplines have been under-researched. To address these issues, this study adopted a cognitive information-theoretic approach to investigate the diachronic change of text difficulty of research article abstracts across the areas of natural sciences, social sciences, and humanities. 1890 abstracts were sampled over a period of 21 years, and two indexes, i.e. entropy from information theory and mean dependency distance from cognitive science, were employed for the calculation of cognitive encoding/decoding difficulty. The results show that in general, the cognitive encoding difficulty of abstracts has been increasing in the past two decades, while the cognitive decoding difficulty of abstracts has been decreasing. Regarding the disciplinary variations, the humanities show no significant diachronic change in encoding difficulty, and the social sciences show no significant diachronic change in decoding difficulty. These phenomena can be attributed to the traits of abstracts, the nature of academic knowledge, the cognitive mechanism in human languages and the features of different disciplines. This study has implications for the innovations in theories and methods of measurement of text difficulty, as well as an in-depth understanding of the disciplinary variations in academic writing and the essence of research article abstracts for research article writers, readers, the scientific community, and academic publishers.

Mechanistic Study and Conceptual Chemical Reactivity Analysis of Hydroboration of Carbon Dioxide Catalyzed by a Manganese(I)-PNP-Pincer Complex.

Designing efficient and selective catalysts for carbon dioxide reduction is an intensive research area in the recent literature on homogeneous catalysis. In this work, we study the catalytic activity of a newly reported Mn(I)-PNP-pincer catalyst with an embedded aromatic ring. First, we systematically examine its capability to yield different products and highlight the importance of ligand aromaticity and steric effects on metal-ligand cooperativity. We then further conceptually probe its reactivity with descriptors from both conceptual density functional theory and an information-theoretic approach, thereby proposing a novel partitioning of the reaction coordinate into three relevant regions. Our results show that the reactivity in these different regions is governed by different properties such as steric effects, electrophilicity/nucleophilicity, or aromaticity. We anticipate that this methodology, with the analytical tools employed in this study, can be generalized and extended to other catalytic systems and find applications in designing better catalysts.

Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation

A concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with the dual capability of exploiting current-estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimizes the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties, such as the convergence of CL-DCEE algorithm, are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time.

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.

Whole-brain network transitions within the framework of ignition and transfer entropy following VIM-MRgFUS in essential tremor patients

Magnetic resonance-guided focused ultrasound (MRgFUS) lesioning of the ventralis intermedius nucleus (VIM) has shown promise in treating drug-refractory essential tremor (ET). It remains unknown whether focal VIM lesions by MRgFUS have broader restorative effects on information flow within the whole-brain network of ET patients. We applied an information-theoretical approach based on intrinsic ignition and the concept of transfer entropy (TE) to assess the spatiotemporal dynamics after VIM-MRgFUS. Eighteen ET patients (mean age 71.44 years) underwent repeated 3T resting-state functional magnetic resonance imaging combined with Clinical Rating Scale for Tremor (CRST) assessments one day before (T0) and one month (T1) and six months (T2) post-MRgFUS, respectively. We observed increased whole brain ignition-driven mean integration (IDMI) at T1 (p < 0.05), along with trend increases at T2. Further, constraining to motor network nodes, we identified significant increases in information-broadcasting (bilateral supplementary motor area (SMA) and left cerebellar lobule III) and information-receiving (right precentral gyrus) at T1. Remarkably, increased information-broadcasting in bilateral SMA was correlated with relative improvement of the CRST in the treated hand. In addition, causal TE-based effective connectivity (EC) at T1 showed an increase from right SMA to left cerebellar lobule crus II and from left cerebellar lobule III to right thalamus. In conclusion, results suggest a change in information transmission capacity in ET after MRgFUS and a shift towards a more integrated functional state with increased levels of global and directional information flow.

Real-world effectiveness and factors associated with effectiveness of inactivated SARS-CoV-2 vaccines: a systematic review and meta-regression analysis

BackgroundThe two inactivated SARS-CoV-2 vaccines, CoronaVac and BBIBP-CorV, have been widely used to control the COVID-19 pandemic. The influence of multiple factors on inactivated vaccine effectiveness (VE) during long-term use and against variants is not well understood.MethodsWe selected published or preprinted articles from PubMed, Embase, Scopus, Web of Science, medRxiv, BioRxiv, and the WHO COVID-19 database by 31 August 2022. We included observational studies that assessed the VE of completed primary series or homologous booster against SARS-CoV-2 infection or severe COVID-19. We used DerSimonian and Laird random-effects models to calculate pooled estimates and conducted multiple meta-regression with an information theoretic approach based on Akaike’s Information Criterion to select the model and identify the factors associated with VE.ResultsFifty-one eligible studies with 151 estimates were included. For prevention of infection, VE associated with study region, variants, and time since vaccination; VE was significantly decreased against Omicron compared to Alpha (P = 0.021), primary series VE was 52.8% (95% CI, 43.3 to 60.7%) against Delta and 16.4% (95% CI, 9.5 to 22.8%) against Omicron, and booster dose VE was 65.2% (95% CI, 48.3 to 76.6%) against Delta and 20.3% (95% CI, 10.5 to 28.0%) against Omicron; primary VE decreased significantly after 180 days (P = 0.022). For the prevention of severe COVID-19, VE associated with vaccine doses, age, study region, variants, study design, and study population type; booster VE increased significantly (P = 0.001) compared to primary; though VE decreased significantly against Gamma (P = 0.034), Delta (P = 0.001), and Omicron (P = 0.001) compared to Alpha, primary and booster VEs were all above 60% against each variant.ConclusionsInactivated vaccine protection against SARS-CoV-2 infection was moderate, decreased significantly after 6 months following primary vaccination, and was restored by booster vaccination. VE against severe COVID-19 was greatest after boosting and did not decrease over time, sustained for over 6 months after the primary series, and more evidence is needed to assess the duration of booster VE. VE varied by variants, most notably against Omicron. It is necessary to ensure booster vaccination of everyone eligible for SARS-CoV-2 vaccines and continue monitoring virus evolution and VE.Trial registrationPROSPERO, CRD42022353272.

Environment Affects Sucker Catch Rate, Size Structure, Species Composition, and Precision in Boat Electrofishing Samples

Abstract Catostomidae (catostomids) are suckers of the order Cypriniformes, and the majority of species are native to North America; however, species in this group are understudied and rarely managed. The popularity in bowfishing and gigging for suckers in the United States has increased concerns related to overfishing. Little information exists about the relative gear effectiveness for sampling catostomids. We sought to evaluate the relative effectiveness of boat electrofishing for sampling Black Redhorse Moxostoma duquesnei, Golden Redhorse M. erythrurum, Northern Hogsucker Hypentelium nigricans, White Sucker Catostomus commersonii, and Spotted Sucker Minytrema melanops populations in Lake Eucha, Oklahoma. We used an information theoretic approach to determine the abiotic variables related to sucker catch per effort (C/f). Our analysis indicated that sucker C/f was highest during the night and decreased with increasing water temperature. Sucker size structure was significantly different between daytime and nighttime samples; however, effect size estimates for size structure comparisons indicated that size distributions exhibited moderate overlap. Distributional comparisons indicated that daytime and nighttime samples were similar for fish greater than 180 mm in total length. Effect size estimates also indicated little association between the proportion of each species captured and time of day or water temperature. Night electrofishing in reservoirs at water temperatures from 16 to 25°C yielded the most precise C/f estimates, with the highest numbers of suckers collected at water temperatures from 6 to 15°C. Further study of the relationship between abiotic variables and catostomid catchability using various gears will be beneficial to agencies interested in these populations.

Disentangling drivers of soil organic carbon storage in deltaic rice paddies from the Ebro Delta

Paddy farming can potentially sequester carbon. However, agricultural practices may alter the stability of the soil organic carbon (SOC) stocks and thereby increase carbon mobilization in the topsoil and subsoil. We hypothesize that both agricultural practices and soil physical–chemical characteristics, mostly those related to the type of the parental soil where the paddy is established, play an important role in SOC stabilization and sequestration. To test this, we profiled the biochemical characteristics of soil organic matter (SOM) and the SOC concentrations in sediment cores of 10 rice fields from the Ebro Delta (Northeast Spain) representing former reclaimed habitats (i.e., peatlands, meadows, coastal lagoons, riverbanks and salt marshes). The effects of physical–chemical soil properties on SOM content were tested with Generalized Linear Models and the best models were selected using information-theoretic approach. The optical characteristics of SOM extracts were analyzed by UV–Visible and fluorescence spectrophotometry, and six fluorescence components were identified by Parallel Factor Analysis (PARAFAC). Higher clay and lower sand content explained the greater SOM presence in topsoil while higher clay content and salinity prevailed as the main explanatory factors in the subsoil showing a positive correlation with SOM content. The results revealed a correlated gradient of SOM quantity and quality where in SOM-rich soils both the aromaticity and the humification degree were higher while in soils with lower SOM content it was fresher and more microbially derived. Current agricultural practices favored SOM humification in the topsoil while the variability in SOM quality in subsoil is attributed to the combination of soil physical–chemical characteristics (pH, conductivity and clay content) and the previous habitat’s influence. This study represents a comprehensive empirical analysis on the carbon stocks in rice fields and the identification of soil physical–chemical factors, related to prior land uses, favoring soil carbon accumulation. These results may have major implications in decision-making process for climate change mitigation in vulnerable Mediterranean coastal paddies.

Cost-constrained feature selection in multilabel classification using an information-theoretic approach

Feature selection is one of the key steps in building a predictive model in multi-label classification. However, most of the existing methods do not take into account information about the costs associated with considered features, such as the costs of performing diagnostic medical tests. We consider a problem of cost-constrained multilabel feature selection, which aims to select a feature subset relevant to multiple labels while satisfying a user-specific maximal admissible budget. This approach allows for building a model with high predictive power, for which the cost of making a prediction for a single instance does not exceed the user-specified budget. In this problem, the balance between the feature subset relevance and its cost should be considered concurrently, which is nontrivial in practice because their optimal balance is unknown. In this paper, we propose a novel criterion for cost-constrained multilabel feature selection that combines the relevance and cost of the candidate feature. The relevance measure is derived using the lower bound of the mutual information between the feature subset and label vector. Moreover, we propose an effective method for determining the cost-factor value that controls the trade-off between relevancy and cost. The experimental results on multilabel datasets with various characteristics demonstrate the superiority of the proposed method over conventional methods.

Do names echo semantics? A large-scale study of identifiers used in C++’s named casts

Developers relax restrictions on a type to reuse methods with other types. While type casts are prevalent, in weakly typed languages such as C++, they are also extremely permissive. Assignments where a source expression is cast into a new type and assigned to a target variable of the new type, can lead to software bugs if performed without care. In this paper, we propose an information-theoretic approach to identify poor implementations of explicit cast operations. Our approach measures accord between the source expression and the target variable using conditional entropy. We collect casts from 34 components of the Chromium project, which collectively account for 27MLOC and random-uniformly sample this dataset to create a manually labelled dataset of 271 casts. Information-theoretic vetting of these 271 casts achieves a peak precision of 81% and a recall of 90%. We additionally present the findings of an in-depth investigation of notable explicit casts, two of which were fixed in recent releases of the Chromium project.

Tropical rays are intrinsically more sensitive to overfishing than the temperate skates

Overfishing, habitat loss, and climate change are driving population declines in many species. Understanding a species' capacity to recover from these and other threats is necessary for prioritising management. The maximum intrinsic rate of population increase (rmax) can be used to compare which species or groups are particularly sensitive to ongoing threats. To investigate global patterns of intrinsic sensitivity of rays and skates (superorder Batoidea), we calculated rmax of 85 species using a modified Euler-Lotka model that accounts for survival to maturity. We examined how rmax varies with body mass, temperature, and depth using an information-theoretic approach through model selection, accounting for phylogenetic non-independence. Although we observed an overall positive relationship between rmax and temperature, we found that warm, shallow-water rays were more intrinsically sensitive to exploitation (lower rmax) than cold, deep-water skates (higher rmax). We hypothesise that this pattern is likely driven by their different reproductive strategies as live-bearing rays have fewer offspring compared to egg-laying skates, and caution that future research should focus on understanding differences in the mortality schedule of juveniles and sub-adults to understand if survival to maturity is comparable. Our findings highlight the high vulnerability of warm, shallow-water ray species to overexploitation and other threats due to their intrinsically low maximum population growth rates. These differences in rmax have conservation implications for our understanding of the geographic patterns in extinction risk, suggesting that tropical rays are more intrinsically sensitive.