Complex Information Processing Research Articles

Can sell-side analysts’ experience, expertise and qualifications help mitigate the adverse effects of accounting reporting complexity?

We examine the relation between accounting reporting complexity and analysts’ performance and whether analysts’ qualifications, experience, and expertise in specific financial domains help them more effectively process complex information. We document an inverse relation between complexity and analysts’ performance. Further, we show that analysts’ firm-specific experience, industry focus, and CFA certification alleviate some of the adverse effects of complexity, whereas analysts’ general experience does not appear to do so. Using an XBRL-based approach, we also develop new measures of analysts’ expertise and find that expertise in the areas of fair value, derivatives and pension are more valuable than other analyst characteristics in attenuating the negative effects of complexity arising from transactions and events in these areas. Overall, this study underscores the importance of analyst characteristics and the need to simplify the complex disclosures in the notes to the financial statements.

Artificial Intelligence in Pharma

This paper aims to provide a better understanding of the possible applications of Artificial Intelligence (AI) in the field of Pharmacy. Artificial intelligence basically involves complex information processing. It enables machines to carry out certain functions which were originally done by human and which required human intellect. Over the past few years, the use of artificial intelligence in the pharma has grown unimaginably and is now able to revolutionize the way drugs are discovered and handled. AI is capable of impacting the field of pharmacy at multiple levels. Pharmacy and medical education, drug development, personalization of drug therapy, drug safety, rational drug use, manufacture and formulation of dosage forms are areas of impact by AI. The fact that AI can displace humans at their own tasks is a matter of concern. Although there are ethical concerns regarding the implementation of AI in healthcare, it can be assured that no machine or system can replace many of the humanitarian duties.

Seven computations of the social brain.

The social environment presents the human brain with the most complex information processing demands. The computations that the brain must perform occur in parallel, combine social and nonsocial cues, produce verbal and nonverbal signals and involve multiple cognitive systems, including memory, attention, emotion and learning. This occurs dynamically and at timescales ranging from milliseconds to years. Here, we propose that during social interactions, seven core operations interact to underwrite coherent social functioning; these operations accumulate evidence efficiently—from multiple modalities—when inferring what to do next. We deconstruct the social brain and outline the key components entailed for successful human–social interaction. These include (i) social perception; (ii) social inferences, such as mentalizing; (iii) social learning; (iv) social signaling through verbal and nonverbal cues; (v) social drives (e.g. how to increase one’s status); (vi) determining the social identity of agents, including oneself and (vii) minimizing uncertainty within the current social context by integrating sensory signals and inferences. We argue that while it is important to examine these distinct aspects of social inference, to understand the true nature of the human social brain, we must also explain how the brain integrates information from the social world.

Cognitive Improvement and Safety Assessment of a Dietary Supplement Containing Propolis Extract in Elderly Japanese: A Placebo-Controlled, Randomized, Parallel-Group, Double-Blind Human Clinical Study.

Objectives. This study aimed to evaluate the effect of propolis on cognitive function in elderly Japanese with a placebo-controlled design. Material and Methods. This study was performed on 79 elderly Japanese. Participants orally received either a placebo or dietary supplement containing propolis extract for 24 weeks. Cognitive function assessed by Cognitrax and various blood or urine markers were measured at pre- and postadministration. Results and Conclusion. Eligible data from 68 subjects (placebo: 33, propolis: 35) who completed the study were analyzed. Compared to the placebo group, the propolis group showed significant improvement in verbal memory in Cognitrax (P=0.028). Total cholesterol, LDL cholesterol, urea nitrogen, creatinine, and uric acid were significantly improved in the propolis group compared to the placebo group (P = 0.011, P = 0.004, P = 0.048, P = 0.045, and P = 0.005, respectively). However, urea nitrogen, creatinine, and uric acid fluctuated within the normal level. Furthermore, a subgroup analysis was performed on those with higher than 100 of the standardized score of the neurocognitive index indicated by the overall Cognitrax score. Significant improvements in the propolis group compared to placebo were confirmed in verbal memory (P = 0.007) and processing speed as indications for information processing ability, complex attention, and concentration (P = 0.029). No side effects were observed in any of the groups. This study demonstrates that propolis is effective in improving cognitive functions such as memory, information processing, complex attention, and concentration in elderly Japanese.

Graphene oxide based synaptic memristor device for neuromorphic computing

Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.

Brain resilience across the general cognitive ability distribution: Evidence from structural connectivity.

Resting state functional connectivity research has shown that general cognitive ability (GCA) is associated with brain resilience to targeted and random attacks (TAs and RAs). However, it remains to be seen if the finding generalizes to structural connectivity. Furthermore, individuals showing performance levels at the very high area of the GCA distribution have not yet been analyzed in this regard. Here we study the relation between TAs and RAs to structural brain networks and GCA. Structural and diffusion-weighted MRI brain images were collected from 189 participants: 60 high cognitive ability (HCA) and 129 average cognitive ability (ACA) individuals. All participants completed a standardized fluid reasoning ability test and the results revealed an average HCA-ACA difference equivalent to 33 IQ points. Automated parcellation of cortical and subcortical nodes was combined with tractography to achieve an 82 × 82 connectivity matrix for each subject. Graph metrics were derived from the structural connectivity matrices. A simulation approach was used to evaluate the effects of recursively removing nodes according to their network centrality (TAs) versus eliminating nodes at random (RAs). HCA individuals showed greater network integrity at baseline and prior to network collapse than ACA individuals. These effects were more evident for TAs than RAs. The networks of HCA individuals were less degraded by the removal of nodes corresponding to more complex information processing stages of the PFIT network, and from removing nodes with larger empirically observed centrality values. Analyzed network features suggest quantitative instead of qualitative differences at different levels of the cognitive ability distribution.

Comprehensibility and Accountability

Abstract In Incomprehensible!, Wendy Wagner highlights a blind spot in the design of many legal programmes: they demand that market players share as much information as possible, but neglect to demand that the information be conveyed comprehensibly. Wagner shows how the neglect of comprehensibility undermines the law’s effectiveness, and provides a menu of concrete proposals for alleviating it. Yet Wagner’s analysis leaves one important question unanswered: will making information more comprehensible necessarily lead to more accountability and better policy outcomes? This review article examines the elusive link between comprehensibility and accountability, and spotlights four common scenarios whereby the former is not a sufficient or even a necessary condition for the latter. In some markets, we have accountability without comprehensibility, through the threat of reputational sanctions. In others, we have comprehensibility without accountability: disclosures are perfectly readable, yet the end users opt not to read them. A third scenario is where more comprehensibility actually leads to less accountability: top-down mandates of comprehensibility crowd out efforts by private intermediaries to process complex information. Finally, there are circumstances where comprehensibility leads to too much accountability: in certain trade-offs, more accountability leads to worse decision making.

Upconversion Luminescence-Controlled DNA Computation for Spatiotemporally Resolved, Multiplexed Molecular Imaging.

DNA-based molecular circuits able to perform complex information processing in biological systems are highly desirable. However, conventional DNA circuits are constitutively always in an ON state and immediately operate when they meet the biomolecular inputs, precluding precise molecular computation at a desired time and in a desired site. In this work, we report a conceptual methodology for the construction of photonic nanocircuits that enable DNA molecular computation in vitro and in vivo with high spatial precision. Upon remote activation by spatially restricted NIR-light input, two types of cancer biomarker inputs can sequentially trigger conformational changes of the DNA circuit through a structure-switching aptamer and toehold-mediated strand exchange, leading to release of a signaling output. Of note, the NIR-light-gated nanocircuit allows for intended control over the specific timing and location of DNA computation, providing spatial and temporal capabilities for multiplexed imaging. Furthermore, an OR-AND-gated nanocircuit of higher complexity was designed to illustrate the versatility of our approach. The present work illustrates the potential of the use of upconversion nanotechnology as a regulatory tool for spatial and temporal control of DNA computation in cells and animals.

The Future of Employment Revisited: How Model Selection Determines Automation Forecasts

The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Prominently, Frey and Osborne (2017) estimated that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings regarding the degree of job automation is, to a large extent, driven by model selection and not by controlling for personal characteristics or tasks. For our case study, we consult experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.

Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement Using Functional MRI.

Background: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions.Objective: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.Material and Methods: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software.Results: The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p< 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions. Conclusion: Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.

Nonlinear stochastic estimation of altitude and vertical flight speed in real time

The optimal algorithm for calculating the altitude and vertical flight speed of an aircraft, which is based on complex processing of information from static pressure sensors, outdoor air temperature, and vertical acceleration is described. Information processing is performed using nonlinear estimation methods. Adaptive properties of the estimation algorithm are achieved by including into the state vector the parameters that characterize the properties of sensors and static pressure transmission paths.

Method and algorithms of complex information processing onboard the aircraft for the benefit of definition of accessory of the purposes

Formulation of the problem. On board an aircraft, the solution of situational awareness tasks can be achieved as a result of the creation of an integrated multi-sensor information environment for a complex of onboard equipment, including sources of information that are heterogeneous in terms of construction principles and nature. One of the difficulties in creating such a multi-sensor information environment is the lack of specialized functional software for the on-board equipment complex, which allows processing and combining signals and information from heterogeneous sources, providing an assessment of the coordinate characteristics of targets in the form of phase coordinates of their spatial position and movement, as well as recognition and identification observed targets by characteristic features. To solve this problem, a method and interconnected algorithms for complex information processing have been developed that would allow combining heterogeneous information about targets, taking into account the conditions and methods of obtaining it when solving problems of tracking, recognition and identification of targets. Goal. Describe the method and interrelated algorithms for integrated information processing in the aircraft onboard equipment complex, which provide tracking, recognition and identification of targets based on information received from heterogeneous OBE sources. Results. The method of complex information processing on board the aircraft is described. A set of interrelated algorithms has been developed, including: an algorithm for the complex adaptive assessment of the phase coordinates of the observed target, conditional relative to its trajectory and signal signs obtained from the results of observations of heterogeneous sources; an algorithm for determining the nationality of the observed target, taking into account the assessment of its class / type based on the results of assessing the values of its trajectory and signal characteristics, as well as the conditions for assessing the phase coordinates. Practical significance. The optimization of the processes of a complex adaptive assessment of the phase coordinates of the observed target, conditional with respect to its trajectory and signal signs, and optimization of the decision-making process about the nationality of the observed target, taking into account the assessment of its class / type based on the values of the trajectory and signal signs of this target, obtained from the results of observations of heterogeneous sources. A variant of the practical implementation of the developed method and algorithms in the OBE of the aircraft is proposed.

Implementation of a single-photon fully quantum router with cavity QED and linear optics

Both cavity quantum electrodynamics and linear optics are promising platforms for the implementation of coherent manipulations of quantum information, and have potential applications in the development of practical quantum devices. In this paper, we propose a simple and deterministic scheme for implementing a single-photon fully quantum router by a combination of both platforms. With this router, a polarization encoded signal photonic qubit can be coherently routed to one of two distinct spatial output ports or their coherent superposition according to the state of a control photonic qubit, while keeping the signal information stored in the signal qubit unaltered under the quantum routing operation. Our scheme has several advantages in contrast to previously published ones, and on top of that, this device meets all the requirements imposed on a fully featured quantum router and is easily scalable for multiqubit quantum routing. Moreover, it exhibits high experimental feasibility with current or near-future technologies. Therefore, this router can be integrated with protocols for the realization of quantum information processing and future complex quantum networks.

МОДЕРНІЗАЦІЯ БІЗНЕС-ПРОЦЕСІВ НА ПІДПРИЄМСТВІ

The article describes the main approaches to understanding the concept of "digital transformation"and the relevance of the process of digital transformation of business processes. Digital transformation is radically reshaping the global business landscape. Business leaders are leveraging key levers of digital transformation practically every industry, including social platforms, mobility, cloud, and analytics, to increase productivity and customer reach for their businesses. The enactment and use such disruptive technologies at the right time and in the right place, organizations can increase their agility, customer service, and identify new business opportunities. Digital transformation is a new stage in business process management, and it is changing the way we do business. Using digital transformation, organizations can streamline their existing operations, explore new business opportunities, expand their business, and gain valuable insights to make the right decisions that help improve customer service. Business processes in our age of digital transformation not only deal with internal processes, but also take into account the expectations of new generation customers. Managed by customers, supported by the IT department and has efficient processes, the digital form of BPM allows you to increase business efficiency and customer service. One of the main problems of strategic analysis in the organization is the complexity of information processing due to its diversity and large volume. It is extremely important to rely on consolidated data in detailed or aggregated form for prompt decision-making. The use of information processing information systems allows organizations to process, transform and organize large amounts of unstructured information to obtain a clear interpretation of complex processes. Interactive reports on sales, marketing, finance, personnel, procurement, suppliers and any other areas allow to comprehensively assess the results of the company. The paper discusses the possibilities of using a business intelligence system for small and medium-sized businesses and suggests ways to optimize the configuration of this system to improve its efficiency. The results of the work are based on practical experience, gained through the creation and implementation of methods for collecting and processing information based on the Power BI system for organizations from the retail sector.

Improving the Civil-Military Relationship: Diversity and the U.S. Army

Although the U.S. Army has significantly increased racial diversity in the aggregate, non-white Americans remain underrepresented as commissioned officers, especially in the General Officer ranks. This is a significant defense policy problem because research shows a strong correlation among the diversity of senior leaders and effectively processing complex information to generate innovative, creative, and efficient solutions. These positive traits of diversity will help General Officers prepare for and manage the long-term and strategic threats posed by peer or near-peer competitors and violent non-state actors. Increased diversity also improves the civil-military relationship by ensuring Army Generals mirror the nation they serve and the soldiers they lead. This paper addresses the lack of racial diversity among U.S. Army General Officers by using descriptive statistical analysis to outline racial diversity trends over the past 10-years and then identifying where and how policies can be most effective. The paper’s findings provide policy makers three options to increase U.S. Army General’s racial diversity.

A synthetic pathogen mimetic molecule induces a highly amplified synergistic immune response via activation of multiple signaling pathways.

The current understanding of how the immune system processes complex information during natural infections is yet to be exploited for the molecular design of potent immune activators. Here, we address this challenge by design of a pathogen-mimetic molecule that simultaneously co-activates cell-surface active, endosomal and cytosolic immune receptors.

Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques

Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naive Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.

Information processing using an integrated DNA reaction network.

Living organisms use interconnected chemical reaction networks (CRNs) to exchange information with the surrounding environment and respond to diverse external stimuli. Inspired by nature, numerous artificial CRNs with a complex information processing function have been recently introduced, with DNA as one of the most attractive engineering materials. Although much progress has been made in DNA-based CRNs in terms of controllable reaction dynamics and molecular computation, the effective integration of signal translation with information processing in a single CRN remains to be difficult. In this work, we introduced a stimuli-responsive DNA reaction network capable of integrated information translation and processing in a stepwise manner. This network is designed to integrate sensing, translation, and decision-making operations by independent modules, in which various logic units capable of performing different functions were realized, including information identification (YES and OR gates), integration (AND and AND-AND gates), integration-filtration (AND-AND-NOT gate), comparison (Comparator), and map-to-map analysis (Feynman gate). Benefitting from the modular and programmable design, continuous and parallel processing operations are also possible. With the innovative functions, we show that the DNA network is a highly useful addition to the current DNA-based CRNs by offering a bottom-up strategy to design devices capable of cascaded information processing with high efficiency.

Comparative Functional Anatomy of Marmoset Brains.

Marmosets and closely related tamarins have become popular models for understanding aspects of human brain organization and function because they are small, reproduce and mature rapidly, and have few cortical fissures so that more cortex is visible and accessible on the surface. They are well suited for studies of development and aging. Because marmosets are highly social primates with extensive vocal communication, marmoset studies can inform theories of the evolution of language in humans. Most importantly, marmosets share basic features of major sensory and motor systems with other primates, including those of macaque monkeys and humans with larger and more complex brains. The early stages of sensory processing, including subcortical nuclei and several cortical levels for the visual, auditory, somatosensory, and motor systems, are highly similar across primates, and thus results from marmosets are relevant for making inferences about how these systems are organized and function in humans. Nevertheless, the structures in these systems are not identical across primate species, and homologous structures are much bigger and therefore function somewhat differently in human brains. In particular, the large human brain has more cortical areas that add to the complexity of information processing and storage, as well as decision-making, while making new abilities possible, such as language. Thus, inferences about human brains based on studies on marmoset brains alone should be made with a bit of caution.

MIND MAP ЯК ЗАСІБ ВІЗУАЛІЗАЦІЇ ТЕХНОЛОГІЧНИХ ПРОЦЕСІВ

A strong increase in the amount of information requires improved approaches to its location and transmission in the educational process. One way to overcome this problem is to use visualization tools, including mind maps, which have didactic effectiveness, especially in combination with the capabilities of computer technology. The aim of the article is to study the modern possibilities of visualization of scientific information during the study of technical disciplines. The concept of visualization, which has become an integral part of processing complex information about the structure of the studied objects, is analyzed; methods, principles and scientific approaches to visualization. The expediency and efficiency of using a mind map to visualize technological processes are substantiated. It is proved that the use of computer mental maps helps to increase the level of memorization of information by detailing the basic concepts, their systematization, classification and generalization; formation of skills of work with graphic information. The conditions of effective use of multimedia mental maps are determined, the observance of which harmonizes the educational process for all its participants. The advantages of using mental maps are determined, among which – active comprehension of educational material in the process of independent creation of own mind maps, which gives the chance to actively assimilate scientific information, acquire skills of its independent structuring, trace logic of connections of various units, find new ideas and develop associative thinking. . The use of visualization tools, including intelligence maps, in the educational process is possible both during the study of new material in lectures, independent work and during the control of assimilation and understanding of scientific information, establishing links between its components, the ability to structure information. Mind map is a productive alternative to traditional methods of processing and transmitting information in the educational process, which turns the student into an active student of higher education. Key words: scientific information, visualization, mind map, technological process.