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Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

Over the past decade, unmanned aerial vehicles (UAVs) have received increasing attention and are being used in the areas of harvesting, videotaping, and the military industry. In this article, the consideration is focused on areas where video recording is required for ground inspections. This paper describes modern communication technologies and systems that enable interaction and data exchange between UAVs and a ground control station (GCS). This article focuses on different architectures of communication systems, establishing the characteristics of each to identify the preferred architecture that does not require a significant consumption of resources and whose data transmission is reliable. A coherent architecture that includes multiple UAVs, wireless sensor networks, cellular networks, GCSs, and satellite network to duplicate communications for enhanced system security has been offered. Some reliability problems have been discussed, the solution of which was suggested to be a backup connection via satellite, i.e., a second connection. This study focused not only on the communication channels but also on the data exchanged between system components, indicating the purpose of their application. Some of the communication problems and shortcomings of various systems, as well as further focus areas and improvement recommendations were discussed.

Information exchange in the human brain is crucial for vital tasks and to drive diseases. Neuroimaging techniques allow for the indirect measurement of information flows among brain areas and, consequently, for reconstructing connectomes analyzed through the lens of network science. However, standard analyses usually focus on a small set of network indicators and their joint probability distribution. Here, we propose an information-theoretic approach for the analysis of synthetic brain networks (based on generative models) and empirical brain networks, and to assess connectome’s information capacity at different stages of dementia. Remarkably, our framework accounts for the whole network state, overcoming limitations due to limited sets of descriptors, and is used to probe human connectomes at different scales. We find that the spectral entropy of empirical data lies between two generative models, indicating an interpolation between modular and geometry-driven structural features. In fact, we show that the mesoscale is suitable for characterizing the differences between brain networks and their generative models. Finally, from the analysis of connectomes obtained from healthy and unhealthy subjects, we demonstrate that significant differences between healthy individuals and the ones affected by Alzheimer’s disease arise at the microscale (max. posterior probability smaller than 1%) and at the mesoscale (max. posterior probability smaller than 10%)., Author Summary Capitalizing on network information theory we propose a new framework for the analysis and comparison of empirical and synthetic human connectomes, in health and disease. We quantitatively assess the ability of our approach in identifying differences and similarities in the structural connectivity of the human brain, ranging between microscopic and macroscopic scales. Relying on two different diffusion processes, namely classic and maximal entropy random walks, we show that the mesoscale is suitable for capturing differences between empirical connectomes and their synthetic models, while differences between healthy subjects and patients affected by Alzheimer’s disease are mostly significant at the microscopic scale.

Video games are a widespread leisure activity and essential for a substantial field of research. In several kinds of research, video games show positive effects on cognition. Video games’ ability to change the brain in a way that improves cognition is already evident in the research world. The underlying brain dynamics assessed by coherence (Coh) and partial-directed coherence (PDC) can shed light on the effect of video game playing. Here, resting-state brain dynamics have been analyzed before and after four weeks of video game playing. Fifteen participants took part in this article, which ran for one consecutive month. Participants played a gem-swapping game with a brain–computer interface (BCI) paradigm for five days per week for approximately 90 min for four continuous weeks. Significant ( $p ) changes in information flow and connectivity measures for Coh and PDC were found in the fronto-central, fronto-parietal, and centro-parietal networks due to extended interaction with BCI. The results suggest that BCI is a potential facilitator of such resting-state network changes and may help to develop new strategies for improving cognition, but we also cannot deny the possible effects of such an effort on the disruption of a player’s sense of engagement and increased mental fatigue.

Multivoxel pattern analysis (MVPA) has become a standard tool for decoding mental states from brain activity patterns. Recent studies have demonstrated that MVPA can be applied to decode activity patterns of a certain region from those of the other regions. By applying a similar region-to-region decoding technique, we examined whether the information represented in the visual areas can be explained by those represented in the other visual areas. We first predicted the brain activity patterns of an area on the visual pathway from the others, then subtracted the predicted patterns from their originals. Subsequently, the visual features were derived from these residuals. During the visual perception task, the elimination of the top-down signals enhanced the simple visual features represented in the early visual cortices. By contrast, the elimination of the bottom-up signals enhanced the complex visual features represented in the higher visual cortices. The directions of such modulation effects varied across visual perception/imagery tasks, indicating that the information flow across the visual cortices is dynamically altered, reflecting the contents of visual processing. These results demonstrated that the distillation approach is a useful tool to estimate the hidden content of information conveyed across brain regions.

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24[Formula: see text]cm) channel pairs.

In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN-DE, DE-EN, EN-ZH, and ZH-EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model's information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement., To appear at TACL and to be presented at ACL 2021. Authors' final version

The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

Significance The rapid comprehension of speech is a remarkable but poorly understood human capacity. Central to this process is the integration of the meaning of each word, as it is heard, into the listener’s interpretation of the utterance. Here we focus on the real-time flow of neural activity that underpins this combinatorial process, using multivariate pattern analysis and computational semantic models to discover the contextual constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of future words in the utterance. This combination of methods reveals a continuous information flow across the left-hemisphere language system, strongly constraining the immediate activation of word meanings and providing a neural substrate for seamless real-time speech comprehension., Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., “eat the apple”). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb’s modification of the DO noun’s activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.

The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC., Accepted to ACL-IJCNLP 2021 main conference

Due to individual differences, different subjects have different response to different emotions. Only using the electroencephalogram (EEG) data that can best describe emotions, can the constructed brain networks better reflect the brain connection patterns. This paper proposes a window selection method of time-varying sequence to select critical EEG data that are most relevant to emotions. The generalized orthogonalized partial directed coherence (gOPDC) method is used to construct effective brain networks based on critical EEG data, which can explore the statistical relationship and the information flow between nodes. At the same time, the false connections caused by volume conduction in EEG channels can also be overcome. We extract network features for brain effective connectivity analysis and emotion recognition. The results of the experiments indicate that emotion is mainly related to the frontal region of the brain, and the parietal lobe also plays an important role in low valence emotions. The connection of brain network is the densest in negative emotions and the least in calm and relaxed emotions. The average classification accuracy reaches 78.78% by using KNN classifier on critical EEG data, and the classification results are better than using all data and the intermediate data.

Studying brain function is a challenging task. In the past, we could only study brain anatomical structures post-mortem, or infer brain functions from clinical data of patients with a brain injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive brain activity observation. Several approaches have been proposed to interpret brain activity data. The brain connectivity model is a graphical tool that represents the interaction between brain regions, during certain states. It depicts how a brain region cause changes to other parts of the brain, which can be implied as information flow. This model can be used to help interpret how the brain works. There are several mathematical frameworks that can be used to infer the connectivity model from brain activity signals. Granger causality is one such approach and is one of the first that has been applied to brain activity data. However, due to the concept of the framework, such as the use of pairwise correlation, combined with the limitation of brain activity data such as low temporal resolution in case of fMRI signal, makes the interpretation of the connectivity difficult. We therefore propose the application of the Tigramite causal discovery framework on fMRI data. The Tigramite framework uses measures such as causal effect to analyze causal relations in the system. This enables the framework to identify both direct and indirect pathways or connectivities. In this paper, we applied the framework to the Human Connectome Project motor task-fMRI dataset. We then present the results and discuss how the framework improves interpretability of the connectivity model. We hope that this framework will help us understand more complex brain functions such as memory, consciousness, or the resting-state of the brain, in the future.

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods., 14 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1904.04745

In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a person in front of it and stores the motion sequences in its memory. Alter3 also uses a self-simulator to simulate its own motions before executing them and generates a self-image. If the visual perception (of a person's motion being imitated) and the imitating self-image differ significantly, Alter3 retrieves a motion sequence closer to the target motion from its memory and executes it. We investigate how this mimetic behavior develops interacting with human, by analyzing memory dynamics and information flow between Alter3 and a interacting person. One important observation from this study is that when Alter3 fails to imitate a person's motion, the person tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. This spontaneous role-switching behavior between a human and Alter3 is a way to initiate personality formation (i.e., personogenesis) in Alter3.

Author(s): Weiser, Sydney Claire | Advisor(s): Ackman, James | Abstract: The neocortex contains a constellation of sensory-motor regions whose functional interactions provide the basis of cognition and behavior. Simultaneously recording neuronal group activity across the cortical hemispheres is essential for understanding the nature of information flow across cerebral networks. Moreover, unbiased and robust methods for measuring functional interactions across the cortex within individual living subjects is critical for substantive tests of genetic and environmental factors that influence brain development and function. To this end, we image pan-neuronally expressed genetically encoded calcium indicators transcranially across the neocortex of unanesthetized, behaving mice throughout development. Recording from behaving mice produces a unique set of challenges, including optical and blood artifacts associated with movement. In addition, areal patterning of the cortex can vary among individuals, ages, and genotypes thus an unbiased, flexible workflow for video acquisition and analysis is necessary for producing high quality segmentations of functional structure across neocortex. To address these challenges, we have developed an eigendecomposition-based workflow that isolates hemodynamic and optical artifacts to recover underlying calcium activity patterns, and segments independent regions of the brain to create maps of functional units across the developing cortex. These unique, data-driven maps provide a reference for understanding developmental, genetic, as well as individual variation between functional units of the brain, and provide a method for extracting optimized time courses from the cortical surface without the need for stimulation-based mapping or anatomical post-processing and alignment. In addition, we quantify the quality of separation of independent sources and use the resulting metrics as feedback to optimize our video acquisition parameters. The open source methods developed here are flexible enough to be utilized with various subject ages and genotypes, as well as numerous experimental configurations and computer architectures. Here we additionally present applications of these methods to anesthesia and development datasets. Overcoming these challenges opens the possibility of using these techniques to help address a number of key objectives in neuroscience, including the quantification of robust inter-areal dynamics between functional motifs across cerebral networks.

ive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.

Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the Flow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation ($r=0.9$) with human ratings among 11 chatbots. Code and pre-trained models will be public. \footnote{\url{https://github.com/ictnlp/DialoFlow}}, Comment: ACL2021 main conference (long paper)

Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.

As information flow in the electronic online environment is increasing, the need for automated processing is increasing. Every minute, 456,000 tweets were written on Twitter. The data in the Mongolian language is already joined in that stream and created their own space since a long time ago. However, in the Mongolian language, there is a lack of research, language resources, and an implemented system by now. In this work, we tried to classify the document level sentiment polarity of Mongolian tweets based on the RNN method of deep learning. The model was tested at the 62,917 SemEval English data, the highest F1 score of 64.7.

Background. One of the main ways to improve the efficiency of information flow compensation is to build a recognition system for the method of information flow compensation for generating reference images. Materials and methods. If a class of systems is characterized by a list of its members, it can be based on the principle of belonging to this list – this method is called the method of comparison with the standard. According to this method, many images belonging to the same class are stored by the identification system. When new images are presented to the system, it consistently compares them with the reference images stored in memory. The system assigns the new image to the class that the reference image that coincided with the presented one belonged to. Results and conclusions. This method works well only when the sample is close to ideal, or the conditions for presenting images are ideal. If members of the same class have some common features, the recognition system is based on the principle of common properties. These General properties are stored in the system memory. During image processing, the system must be able to extract features from the presented image and work with them. The system assigns the newly presented image to a class whose features are similar to those highlighted in the new image.

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away. To address this, we propose K-Adapter, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused model. Taking RoBERTa as the backbone model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus multiple adapters can be efficiently trained in a distributed way. As a case study, we inject two kinds of knowledge in this work, including (1) factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata and (2) linguistic knowledge obtained via dependency parsing. Results on three knowledge-driven tasks, including relation classification, entity typing, and question answering, demonstrate that each adapter improves the performance and the combination of both adapters brings further improvements. Further analysis indicates that K-Adapter captures versatile knowledge than RoBERTa.

It has received increasing attention to exploiting temporal visual features and corresponding descriptions in video captioning. Most of the existing models generate the captioning words merely depend on video temporal structure, ignoring fine-grained complete scene information. And the traditional long-short term memory (LSTM) in recent models is used as decoder to generate sentences, the last generated states in previous hidden ones are used to do work directly to predict the current word. This may lead to the predicted word highly related to the last generated hidden state other than the overall context information. To model the temporal aspects of activities typically shown in the video and better capture long-range context information, we propose a novel video captioning framework via context-guided semantic features model (CSF). Specifically, to maximum information flow, several previous and future information are aggregated to guide the current token by the semantic loss in the encoding and decoding phase respectively. The visual and linguistic information are corrected by fusing the surrounding information. Extensive experiments conducted on MSVD and MSR-VTT video captioning datasets demonstrate the effectiveness of our method compared with state-of-the-art approaches.

Social Manufacturing is a novel approach where different members of a community can interact within a cyber-physical social space in order to achieve specific and personalized solution for manufacturing processes. In such digital scenario, interactions, driven by information flows, can be divided in different branches depending on the actors involved in the whole process. One particularly critic branch for such collective production, is the one that captures the information related with product design, due to its direct link with creativity. Here we show how active tools based on Artificial Intelligence triggers artificial creativity that can be used for the user capabilities augmentation. We show how the use of deep generative models, based on Variational Autoenconders, offers solutions for a particular social manufacturing platform for furniture design combined with additive manufacturing to drive the transition from a digital framework to a real context., {"references":["K. Ding, P. Jiang, and X. Zhang, \"A framework for implementing social manufacturing system based on customized community space configuration and organization,\" Advanced Materials Research, vol. 2450, no. 712, pp. 3191-3194, 2013.","R. Duray, \"Mass customization origins: mass or custom manufacturing?,\" International Journal of Operations Production Management, Vol., pp. 314-328, 2002.","B. D. . F. F. S. Da Silveira, G., \"Mass customization: Literature review and research directions,\" International Journal of Production Economics, 72(1), 1-13, 2001.","B. Mohajeri, \"Paradigm shift from current manufacturing to social manufacturing,\" Aalto University, 2015.","F. Wang, \"From social computing to social manufacturing: The coming industrial revolution and new frontier in cyber-physical-social space,\" Bulletin of Chinese Academy of Sciences, vol. 6, no. 1,pp.658-669, 2012.","P. Jiang, K. Ding, and J. Leng, \"Towards a cyber-physical-socialconnected and service-oriented manufacturing paradigm: Social manufacturing,\" Manufacturing Letters, vol. 7 pp. 15-21, 2016.","J. Parrish and W. Hamner, \"Animal groups in three dimensions: How species aggregate,\" Cambridge University Press, 1997."]}

Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states., Author Summary Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.

Naive Bayes classifier (NBC) is an effective classification technique in data mining and machine learning, which is based on the attribute conditional independence assumption. However, this assumption rarely holds true in real-world applications, so numerous researches have been made to alleviate the assumption by attribute weighting. To the best of our knowledge, almost all studies have calculated attribute weights according to correlation measure or classification accuracy. In this paper, we propose a novel causality-based attribute weighting method to establish the weighted NBC called IFG-WNBC, where causal information flow (IF) theory and genetic algorithm (GA) are adopted to search for optimal weights. The introduction of IF produces a bran-new weight measure criterion from the angle of causality other than correlation. The population initialization in GA is also improved with IF-based weights for efficient optimization. Multi-set of comparison experiments on UCI data sets demonstrate that IFG-WNBC achieves superiority over classic NBC and other common weighted NBC algorithms in classification accuracy and running time.

Effort has been made to find biomarkers for vascular dementia (VaD). Nevertheless, the current findings are typically obtained through statistical tests of group level differences. In clinical practice, however, it is more common to perform individual level inferences, e.g., to determine if a subject is suffering from VaD, which cannot be resolved with statistical analysis. The goal of this study is to develop a method to effectively discriminate early VaD patients from normal controls by combining EEG features with machine learning methods. The EEG signals were recorded from a total of 15 VaD patients and 21 controls during a visual oddball task. Interregional directed connectivity was derived from directed transfer function (DTF) analysis and used as features in classification. Three machine learning methods, including linear discriminant analysis (LDA), error back-propagation (BP) neural network, and support vector machine (SVM) were used as classifiers, and their classification performance was compared. It was found that VaD patients can be effectively identified using the BP and SVM classifiers with high accuracy. In particular, when the SVM classifier was combined with feature selection by Fisher score, it reached an accuracy 86.11%, sensitivity 86.67%, and specificity 85.71%. The area under the curve (AUC, 0.854) indicates a good identification of VaD patients from the normal controls. Since the EEG is noninvasive, inexpensive, and widely available to use, the current study presents a novel clinical application of machine learning methods and could facilitate automatic screening and diagnosis of the VaD at an early stage in future.

How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which several measures are available, foremost among them the Shannon information, which is an undirected measure. Several information-theoretic notions of &ldquo, directed information&rdquo, have been used to successfully detect the flow of information in some systems, in particular in the neuroscience community. However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks (motion detection and sound localization). Besides counting the frequency of problematic logic gates, we also test whether transfer entropy applied to an activity time-series recorded from behaving digital brains can infer information flow, compared to a ground-truth model of direct influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer directed information when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

Many studies have focused on the evolution of communication in artificial life, often presupposing dedicated communication channels such as voices or pheromones. However, it is plausible that only behavior existed when communication began and that some behaviors gradually evolved as a mean of communication. A study by Quinn has shown that communication can evolve purely through the behavior for two agents in a simulated environment. We extend this study to a more complex communication using three agents. In the simulation, the agents are equipped with two wheels and eight IR sensors and are controlled by identical neural networks. This network evolved by applying a genetic algorithm to a single population. After evolution, the agents’ behaviors had differentiated into communicative and goal-directed types. We analyze the differences between these two types of behavior quantitatively, and show that the evolved behaviors have communication properties such as mutuality, information flow, and invariance of the motion gain.

This paper aims to explore the automatic detection method of epileptic seizures to improve the treatment and diagnosis of medically refractory epilepsy patients. A new algorithm based on directed transfer function (DTF) method was proposed for epileptic seizure detection. First, the sliding window technique was used to segment electroencephalogram (EEG) recordings, and the cerebral functional connectivity was calculated by the DTF algorithm. Then, the total information outflow based on the DTF-derived connectivity was calculated by adding up the information flow from a single EEG channel to other channels. Finally, the information outflow was assigned as the features of support vector machine (SVM) classifier to discriminate interictal and ictal EEG segments. For 10 epilepsy patients, the proposed algorithm provided the mean correct rate of 98.45%, the mean selectivity of 64.43%, the mean sensitivity of 93.36%, the mean specificity of 98.42%, and the average detection rate of 95.89%. By applying the statistical analysis, the superiority of DTF-based method was statistically significant when compared with other algorithms in terms of five assessment criteria. Our results indicated that the DTF-derived connectivity could characterize the dynamic causal interaction patterns between brain areas during seizure states, and the proposed method was suitable for the detection of epileptic seizures.

The increasing requirements regarding quality assurance and processes traceability require the implementation of continuous improvement procedures, with a view of maintaining the company’s levels of competitiveness and satisfaction for its customers in terms of quality. The optimization of information flows and manufacturing processes are, in general, extremely important factors in any company’s life. It is therefore important that companies quickly self-adapt to this kind of reality and adopt policies of product innovation and standardization of its production processes, in order to allow rapid adaptation to the constant market changes. The present work aims to identify problems and finding solutions through a combination of PDCA (Plan-Do-Check-Act) cycle, 5S (Seiri, Seiton, Seiso, Seiketsu, Shitzuke) and 5W2H (5 Whys + 2 Hows) tools those can be implemented into a program of continuous improvement, with a view of standardizing of processes, ensuring real profits in products typically with critical added-value. The combination of tools was implemented and the results have been checked, showing a significant impact in the Weaving production process, with gains of 10% in the useful available time by the operator.

The paper presents a theory of information systems based on advanced analogies with both classical and quantum physical models. In the first step the information analogies with magneto-electric circuits are introduced and the information parameters are defined under this inspiration. Well-known potential and flow values (e.g. potential and kinetic energy, voltage and electrical current, etc.) are transformed into information values, “information content” and “information flow”. In the second step the quantum information models are introduced together with values “ware information flow” and “wave information content”. By using these variables, the complex information models are described in more detail together with illustrative examples.

This research explores the effect of the structuredness of design concept generation techniques on temporal network neurocognition. Engineering graduate students (n = 30) completed three concept generation tasks using techniques with different levels of structuredness: brainstorming, morphological analysis, and TRIZ. Students’ brain activation in their prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). The temporal dynamic of central regions in brain networks were compared between tasks. Central regions facilitate functional interaction and imply information flow through the brain. A consistent central region appears in the medial PFC. Consistent network connections occurred across both hemispheres suggesting a concurrent dual processing of divergent and convergent thinking. This study offers novel insights into the underlying neurophysiological mechanism when using these concept generation techniques.

Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm’s ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

Рассмотрен вопрос о создании корпоративной сети в рамках предлагаемой компоновочной схемы технологического парка на базе Сумгаитского государственного университета (СГУ). Определены основные требования эффективного функционирования технологического парка в высших учебных заведениях. Обосновано создание корпоративной сети в технологическом парке на базе функциональных подсистем образовательного, научно-исследовательского, производственного и экономического назначения, которая обеспечивала бы эффективное автоматизированное информационное взаимодействие подсистем технопарка СГУ, а также планирование информационных потоков между этими подразделениями. Предложены структура технического обеспечения на основе выбранных средств для организации надежной корпоративной сети технологического парка, а также структура схематического размещения технических средств корпоративной сети на базе UniFi в технологическом парке СГУ., The problem of creating a corporate network of the proposed composes scheme of the technological park on the basis of the Sumgait State University is considered in the article. The main requirements for the effective functioning of the technological park in higher educational establishments are determined. The creation of a corporate network in a technology park based on functional subsystems of educational, research, production and economic purposes, which would ensure effective automated information interaction of the subsystems of the SSU industrial park, and also planning information flows between these divisions, is justified. The structure of technical support on the basis of the chosen means for the organization of a reliable corporate network of the technological park is proposed. The structure of the schematic deployment of technical units of the corporate network on the basis of UniFi in the technopark of the Sumgait State University is proposed., Розглянуто питання щодо створення корпоративної мережі в межах пропонованої компонувальною схеми технологічного парку на базі Сумгаїтського державного університету (СДУ). Визначено основні вимоги ефективного функціонування технологічного парку у вищих навчальних закладах. Обґрунтовано створення корпоративної мережі в технологічному парку на базі функціональних підсистем освітнього, науково-дослідного, виробничого і економічного призначення, яка забезпечувала б ефективне автоматизоване інформаційну взаємодію підсистем технопарку СДУ, а також планування інформаційних потоків між цими підрозділами. Запропоновано структуру технічного забезпечення на основі обраних засобів для організації надійної корпоративної мережі технологічного парку, а також структуру схематичного розміщення технічних засобів корпоративної мережі на базі UniFi в технопарку СДУ.

Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-level translation of the source content. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks., Comment: Published in COLING2020

Classical accounts of child language learning invoke memory limits as a pressure to discover sparse, language-like representations of speech, while more recent proposals stress the importance of prediction for language learning. In this study, we propose a broad-coverage unsupervised neural network model to test memory and prediction as sources of signal by which children might acquire language directly from the perceptual stream. Our model embodies several likely properties of real-time human cognition: it is strictly incremental, it encodes speech into hierarchically organized labeled segments, it allows interactive top-down and bottom-up information flow, it attempts to model its own sequence of latent representations, and its objective function only recruits local signals that are plausibly supported by human working memory capacity. We show that much phonemic structure is learnable from unlabeled speech on the basis of these local signals. We further show that remembering the past and predicting the future both contribute to the linguistic content of acquired representations, and that these contributions are at least partially complementary.

В статье рассмотрена необходимость использования набора актуальных методик, подходов и технологий, позволяющих обеспечить устойчивую экономическую эффективность, качественное социальное взаимодействие и высокий уровень конкурентных преимуществ предприятия в долгосрочной перспективе в условиях современной цифровой трансформации. Уточнены концептуальные основы подходов к управлению человеческими ресурсами как доминантной категории, обеспечивающей возможность «покрытия» социальнопсихологической компоненты процессов, имеющих место в современных цифровых процессах: динамическом росте объёмов информационных потоков, углублении математических алгоритмов, развитии искусственного интеллекта и муферизации планеты. Выявлены также аспекты негативного содержания, сокращающие положительный эффект цифровизации, такие как киберугрозы и потеря человеческой самоидентификации. Установлена необходимость развития в современных работниках soft-skills – навыков, позволяющих управлять цифровыми процессами с позиции знаний и видения, недоступных искусственному интеллекту., The article discusses the need to use a set of relevant methods, approaches and technologies to ensure sustainable economic efficiency, high-quality social interaction and a high level of competitive advantages of the enterprise in the long term in the context of modern digital transformation. The author specifies the conceptual basis of the approaches to managing human resources as a dominant category that provides the ability to «cover» the socio-psychological component of the operations that take place in modern digital processes: the dynamic growth of information flows, the deepening of mathematical algorithms, the development of artificial intelligence and muffering of the planet. Moreover, the author discloses the aspects of negative content, such as cyber threats and the loss of human self-identification, that diminish the positive effect of digitalization. Furthermore, the article states the necessity of developing the soft-skills that allow modern employees to control digital processes from a position of knowledge and vision that are not accessible to artificial intelligence., Ученые записки Крымского инженерно-педагогического университета, Выпуск 2 (68) 2020

How to generate relevant and informative response is one of the core topics in response generation area. Following the task formulation of neural machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. However, the dialogue model tends to generate safe, commonplace responses (e.g., I don't know) regardless of the input, when learning to maximize the likelihood of response for the given message in an almost loss-less manner just like MT. Different from existing works, we propose a Global-Local Selective Encoding model (GLSE) to extend the seq2seq framework to generate more relevant and informative responses. Specifically, two types of selective gate network are introduced in this work: (i) A local selective word-sentence gate is added after encoding phase of Seq2Seq learning framework, which learns to tailor the original message information and generates a selected input representation. (ii) A global selective bidirectional-context gate is set to control the bidirectional information flow from a BiGRU based encoder to decoder. Empirical studies indicate the advantage of our model over several classical and strong baselines.