Memory effects and static disorder reduce information in single-molecule signals

A key theoretical challenge posed by single-molecule studies is the inverse problem of deducing the underlying molecular dynamics from the time evolution of low-dimensional experimental observables. Toward this goal, a variety of low-dimensional models have been proposed as descriptions of single-molecule signals, including random walks with or without conformational memory and/or with static or dynamics disorder. Differentiating among different models presents a challenge, as many distinct physical scenarios lead to similar experimentally observable behaviors such as anomalous diffusion and nonexponential relaxation. Here we show that information-theory-based analysis of single-molecule time series, inspired by Shannon's work studying the information content of printed English, can differentiate between Markov (memoryless) and non-Markov single-molecule signals and between static and dynamic disorder. In particular, non-Markov time series are more predictable and thus can be compressed and transmitted within shorter messages (i.e. have a lower entropy rate) than appropriately constructed Markov approximations, and we demonstrate that in practice the LZMA compression algorithm reliably differentiates between these entropy rates across several simulated dynamical models.

Spontaneous speech intelligibility: early cochlear implanted children versus their normally hearing peers at seven years of age

Speaking intelligibly is an important achievement in children’s language development. How far do congenitally severe-to-profound hearing-impaired children who received a cochlear implant (CI) in the first two years of their life advance on the path to intelligibility in comparison to children with typical hearing (NH)? Spontaneous speech samples of children with CI and children with NH were orthographically transcribed by naïve transcribers. The entropy of the transcriptions was computed to analyze their degree of uniformity. The same samples were also rated on a continuous rating scale by another group of adult listeners. The transcriptions of the NH children’s speech were more uniform, i.e., had significantly lower entropy, than those of the CI children, suggesting that the latter group displayed lower intelligibility. This was confirmed by the ratings on the continuous scale. Despite the relatively restricted age ranges, older children reached better intelligibility scores in both groups.

MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness

Join operations of data sets play a crucial role in obtaining the relations of massive data in real life. Joining two data sets with MapReduce requires a proper design of the Map and Reduce stages for different scenarios. The factors affecting MapReduce join efficiency include the density of the data sets and data transmission over clusters like Hadoop. This study aims to improve the efficiency of MapReduce join algorithms on Hadoop by leveraging Shannon entropy to measure the information changes of data sets being joined in different MapReduce stages. To reduce the uncertainty of data sets in joins through the network, a novel MapReduce join algorithm with dynamic partition strategies called dynamic partition join (DPJ) is proposed. Leveraging the changes of entropy in the partitions of data sets during the Map and Reduce stages revises the logical partitions by changing the original input of the reduce tasks in the MapReduce jobs. Experimental results indicate that the entropy-based measures can measure entropy changes of join operations. Moreover, the DPJ variant methods achieved lower entropy compared with the existing joins, thereby increasing the feasibility of MapReduce join operations for different scenarios on Hadoop.

Entropy of Badminton Strike Positions

The aim of the current study was twofold: (i) to investigate the distribution of the strike positions of badminton players while quantifying the corresponding standard entropy and using an alternative metric (spatial entropy) related to winning and losing points and random positions; and (ii) to evaluate the standard entropy of the receiving positions. With the datasets of 259 badminton matches, we focused on the positions of players’ strokes and the outcome of each point. First, we identified those regions of the court from which hits were most likely to be struck. Second, we computed the standard entropy of stroke positions, and then the spatial entropy, which also considers the order and clustering of the hitting locations in a two-dimensional Euclidean space. Both entropy quantifiers revealed high uncertainty in the striking position; however, specific court locations (i.e., the four corners) are preferred over the rest. When the outcome of each point was taken into account, we observed that the hitting patterns with lower entropy were associated with higher probabilities of winning points. On the contrary, players striking from more random positions were more prone to losing the points.

The Cultural Evolution of Creative Ideas and Social Innovations

We apply complex systems science to the study of social systems and show how a complex-systems-inspired theory of creativity, which is referred to as ‘honing theory’, provides insight into social innovation. We propose that creativity and social innovation are processes of self-organization that yield a lower-entropy state in worldviews, which are self-organizing webs of understanding. This allows us to offer a novel perspective on the evolution of technology, the role of creativity in cultural evolution and the manner in which creativity drives innovation in social systems, such as the economy. We also introduce creative destruction as having metaphoric relevance for a social system transition from entropy to negentropy, and offer a social innovation example addressing economic collapse and resilient reorganization. We conclude that concepts from complex systems theory, and particularly entropy, shed light on both creativity and social innovation and further our understanding of how innovation affects social systems, such as in cultural and economic change.

Primary Gastro-Intestinal Lymphoma and Gastro-Intestinal Adenocarcinoma: An Initial Study of CT Texture Analysis as Quantitative Biomarkers for Differentiation

Background: To explore the potential role of computed tomography (CT) texture analysis and an imaging biomarker in differentiating primary gastro-intestinal lymphoma (PGIL) from gastro-intestinal adenocarcinoma (GIAC). Methods: A total of 131 patients with surgical pathologically PGIL and GIAC were enrolled in this study. Histogram parameters of arterial and venous phases extracted from contrast enhanced modified discrete cosine transform (MDCT) images were compared between PGIL and GIAC by Mann–Whitney U tests. The optimal parameters for differentiating these two groups were obtained through receiver operating characteristic (ROC) curves and the area under the curve (AUC) was calculated. Results: Compared with GIAC, in arterial phase, PGIL had statistically higher 5th, 10th percentiles (p = 0.003 and 0.011) and statistically lower entropy (p = 0.001). In the venous phase, PGIL had statistically lower mean, median, 75th, 90th, 95th percentiles, and entropy (p = 0.036, 0.029, 0.007, 0.001 and 0.001, respectively). For differentiating PGIL from GIAC, V-median + A-5th percentile was an optimal parameter for combined diagnosis (AUC = 0.746, p < 0.0001), and the corresponding sensitivity and specificity were 81.7 and 64.8%, respectively. Conclusion: CT texture analysis could be useful for differential diagnosis of PGIL and GIAC.

Entropy of Co-Enrolment Networks Reveal Disparities in High School STEM Participation

The current study uses a network analysis approach to explore the STEM pathways that students take through their final year of high school in Aotearoa New Zealand. By accessing individual-level microdata from New Zealand’s Integrated Data Infrastructure, we are able to create a co-enrolment network comprised of all STEM assessment standards taken by students in New Zealand between 2010 and 2016. We explore the structure of this co-enrolment network though use of community detection and a novel measure of entropy. We then investigate how network structure differs across sub-populations based on students’ sex, ethnicity, and the socio-economic-status (SES) of the high school they attended. Results show the structure of the STEM co-enrolment network differs across these sub-populations, and also changes over time. We find that, while female students were more likely to have been enrolled in life science standards, they were less well represented in physics, calculus, and vocational (e.g., agriculture, practical technology) standards. Our results also show that the enrollment patterns of Asian students had lower entropy, an observation that may be explained by increased enrolments in key science and mathematics standards. Through further investigation of differences in entropy across ethnic group and high school SES, we find that ethnic group differences in entropy are moderated by high school SES, such that sub-populations at higher SES schools had lower entropy. We also discuss these findings in the context of the New Zealand education system and policy changes that occurred between 2010 and 2016.

Impact of Antiphase Boundaries on Structural, Magnetic and Vibrational Properties of Fe3Al

We performed a quantum-mechanical study of the effect of antiphase boundaries (APBs) on structural, magnetic and vibrational properties of Fe3Al compound. The studied APBs have the {001} crystallographic orientation of their sharp interfaces and they are characterized by a 1/2⟨111⟩ shift of atomic planes. There are two types of APB interfaces formed by either two adjacent planes of Fe atoms or by two adjacent planes containing both Fe and Al atoms. The averaged APB interface energy is found to be 80 mJ/m2 and we estimate the APB interface energy of each of the two types of interfaces to be within the range of 40–120 mJ/m2. The studied APBs affect local magnetic moments of Fe atoms near the defects, increasing magnetic moments of FeII atoms by as much as 11.8% and reducing those of FeI atoms by up to 4%. When comparing phonons in the Fe3Al with and without APBs within the harmonic approximation, we find a very strong influence of APBs. In particular, we have found a significant reduction of gap in frequencies that separates phonon modes below 7.9 THz and above 9.2 THz in the defect-free Fe3Al. All the APBs-induced changes result in a higher free energy, lower entropy and partly also a lower harmonic phonon energy in Fe3Al with APBs when compared with those in the defect-free bulk Fe3Al.

Stopping Super-Earths from growing into Jupiters

&lt;p&gt;Super-Earths are by far the most dominant type of exoplanet, yet their formation is&lt;br /&gt;still not well understood. In particular, planet formation models predict that many&lt;br /&gt;of them should have accreted enough gas to become gas giants. Here we examine the&lt;br /&gt;role of the protoplanetary disk in the cooling and contraction of the protoplanetary&lt;br /&gt;envelope. In particular, we investigate the effects of 1) the thermal state of the disk as&lt;br /&gt;set by the relative size of heating by accretion or irradiation, and whether its energy is&lt;br /&gt;transported by radiation or convection, and 2) advection of entropy into the outer envelope by disk flows that penetrate the Hill sphere, as found in 3D global simulations.&lt;br /&gt;We find that, at 0.1 AU, the envelope quickly becomes fully radiative, nearly isothermal, and thus cannot cool down, stalling gas accretion. This&lt;br /&gt;effect is significantly more pronounced in convective disks, leading to envelope mass or-&lt;br /&gt;ders of magnitude lower. Entropy advection at 0.1 AU in either radiative or convective&lt;br /&gt;disks could therefore explain why super-Earths failed to undergo runaway accretion.&lt;/p&gt; &lt;p&gt;Ali-Dib, Cumming, &amp; Lin (MNRAS 2020)&lt;/p&gt;

Metaheuristics in the Optimization of Cryptographic Boolean Functions

Generating Boolean Functions (BFs) with high nonlinearity is a complex task that is usually addresses through algebraic constructions. Metaheuristics have also been applied extensively to this task. However, metaheuristics have not been able to attain so good results as the algebraic techniques. This paper proposes a novel diversity-aware metaheuristic that is able to excel. This proposal includes the design of a novel cost function that combines several information from the Walsh Hadamard Transform (WHT) and a replacement strategy that promotes a gradual change from exploration to exploitation as well as the formation of clusters of solutions with the aim of allowing intensification steps at each iteration. The combination of a high entropy in the population and a lower entropy inside clusters allows a proper balance between exploration and exploitation. This is the first memetic algorithm that is able to generate 10-variable BFs of similar quality than algebraic methods. Experimental results and comparisons provide evidence of the high performance of the proposed optimization mechanism for the generation of high quality BFs.