Unidirectional Projection Research Articles

The extended ELECTRE III group decision making method based on regret theory under probabilistic interval-valued hesitant fuzzy environments

The probabilistic interval-valued hesitant fuzzy set (PIVHFS), being able to generalize interval-valued hesitant fuzzy sets embedded with the decision makers (DMs)’ preferences for different elements, can depict uncertain decision information completely and precisely. Therefore, with this study, we propose an extended Elimination and Choice Translating Reality (ELECTRE) III method based on regret theory and Borda rule under a PIVHFS environment, which has the advantage of considering the regret psychology of the DMs and subordinate ranking orders. Using the proposed methodology, a fuzzy bidirectional projection measure is developed to overcome the shortcoming of unidirectional projection. The proposed method is validated by two numerical examples, and the effectiveness of the method is demonstrated through sensitivity and comparative analyses.

Neural circuit pathology driven by Shank3 mutation disrupts social behaviors.

SUMMARYDysfunctional sociability is a core symptom in autism spectrum disorder (ASD) that may arise from neural-network dysconnectivity between multiple brain regions. However, pathogenic neural-network mechanisms underlying social dysfunction are largely unknown. Here, we demonstrate that circuit-selective mutation (ctMUT) of ASD-risk Shank3 gene within a unidirectional projection from the prefrontal cortex to the basolateral amygdala alters spine morphology and excitatory-inhibitory balance of the circuit. Shank3 ctMUT mice show reduced sociability as well as elevated neural activity and its amplitude variability, which is consistent with the neuroimaging results from human ASD patients. Moreover, the circuit hyper-activity disrupts the temporal correlation of socially tuned neurons to the events of social interactions. Finally, optogenetic circuit activation in wild-type mice partially recapitulates the reduced sociability of Shank3 ctMUT mice, while circuit inhibition in Shank3 ctMUT mice partially rescues social behavior. Collectively, these results highlight a circuit-level pathogenic mechanism of Shank3 mutation that drives social dysfunction.

Phase-locking patterns underlying effective communication in exact firing rate models of neural networks.

Macroscopic oscillations in the brain have been observed to be involved in many cognitive tasks but their role is not completely understood. One of the suggested functions of the oscillations is to dynamically modulate communication between neural circuits. The Communication Through Coherence (CTC) theory proposes that oscillations reflect rhythmic changes in excitability of the neuronal populations. Thus, populations need to be properly phase-locked so that input volleys arrive at the peaks of excitability of the receiving population to communicate effectively. Here, we present a modeling study to explore synchronization between neuronal circuits connected with unidirectional projections. We consider an Excitatory-Inhibitory (E-I) network of quadratic integrate-and-fire neurons modeling a Pyramidal-Interneuronal Network Gamma (PING) rhythm. The network receives an external periodic input from either one or two sources, simulating the inputs from other oscillating neural groups. We use recently developed mean-field models which provide an exact description of the macroscopic activity of the spiking network. This low-dimensional mean field model allows us to use tools from bifurcation theory to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We identify the conditions for optimal phase-locking and effective communication. We find that inputs with high coherence can entrain the network for a wider range of frequencies. Besides, faster oscillatory inputs than the intrinsic network gamma cycle show more effective communication than inputs with similar frequency. Our analysis further shows that the entrainment of the network by inputs with higher frequency is more robust to distractors, thus giving them an advantage to entrain the network and communicate effectively. Finally, we show that pulsatile inputs can switch between attended inputs in selective attention.

Bidirectional loss function for Label Enhancement and distribution learning

Label distribution learning (LDL) is an interpretable and general learning paradigm that has been applied in many real-world applications. In contrast to the simple logical vector in single-label learning (SLL) and multi-label learning (MLL), LDL assigns labels with a description degree to each instance. In practice, two challenges exist in LDL, namely, how to address the dimensional gap problem during the learning process of LDL and how to exactly recover label distributions from existing logical labels, i.e., Label Enhancement (LE). For most existing LDL and LE algorithms, the fact that the dimension of the input matrix is much higher than that of the output one is always ignored and it typically leads to the dimensional reduction owing to the unidirectional projection. The valuable information hidden in the feature space is lost during the mapping process. To this end, this study considers bidirectional projections function which can be applied in LE and LDL problems simultaneously. More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one. This loss function aims to potentially reconstruct the input data from the output data. Therefore, it is expected to obtain more accurate results. Experiments on several real-world datasets are carried out to demonstrate the superiority of the proposed method for both LE and LDL. Specifically, BD-LE achieves optimal performance in 85.71% cases and renders sub-optimal in 13.09% cases. BD-LDL ranks 1st in 90.48% cases across six evaluation measurements. Compared with the baseline algorithms, the bidirectional projection methods can outperform the best baselines over 7.38% and 9.98% on average for LE and LDL, respectively.

Resilient peripheralisation through authoritarian communication against energy democracy in South Korea

ABSTRACT To understand anti-democratic barriers to accomplishing energy democracy, I examine how and why a centralised energy system can maintain itself based on an ethnographic analysis of the local protest of the Miryang community against the Korea Electric Power Corporation’s construction of a 765,000-volt transmission line. In doing so, I incorporate the element of authoritarian communication into the framework of peripheralization – a socio-political reproduction of social, spatial, and environmental inequality caused by siting environmentally hazardous facilities in peripheral communities and reinforcing their powerlessness – to emphasize the aggressive role of government and industry actors. Composed of unidirectional projection, partial absorption, and forceful suppression, authoritarian communication makes the sacrifices of peripheral communities enduring, and consequently enables an undemocratic energy arrangement to be resilient and perpetual. To overcome this mechanism, I highlight the active roles of citizens to contest authoritarian communication and socio-environmental inequalities as a precondition to building a sustainable energy democracy.

How Informative Are Spatial CA3 Representations Established by the Dentate Gyrus?

In the mammalian hippocampus, the dentate gyrus (DG) is characterized by sparse and powerful unidirectional projections to CA3 pyramidal cells, the so-called mossy fibers. Mossy fiber synapses appear to duplicate, in terms of the information they convey, what CA3 cells already receive from entorhinal cortex layer II cells, which project both to the dentate gyrus and to CA3. Computational models of episodic memory have hypothesized that the function of the mossy fibers is to enforce a new, well separated pattern of activity onto CA3 cells, to represent a new memory, prevailing over the interference produced by the traces of older memories already stored on CA3 recurrent collateral connections. Can this hypothesis apply also to spatial representations, as described by recent neurophysiological recordings in rats? To address this issue quantitatively, we estimate the amount of information DG can impart on a new CA3 pattern of spatial activity, using both mathematical analysis and computer simulations of a simplified model. We confirm that, also in the spatial case, the observed sparse connectivity and level of activity are most appropriate for driving memory storage – and not to initiate retrieval. Surprisingly, the model also indicates that even when DG codes just for space, much of the information it passes on to CA3 acquires a non-spatial and episodic character, akin to that of a random number generator. It is suggested that further hippocampal processing is required to make full spatial use of DG inputs.

Frame rate up-conversion using multiresolution critical point filters with occlusion refinement

In this paper, multiresolution critical-point filters (CPFs) are employed to image matching for frame rate up-conversion (FRUC). By CPF matching, the dense motion field can be obtained for representing object motions accurately. However, the elastic motion model does not hold in the areas of occlusion, thus resulting in blur artifacts in the interpolated frame. To tackle this problem, we propose a new FRUC scheme using an occlusion refined CPF matching interpolation (ORCMI). In the proposed approach, the occlusion refinement is based on a bidirectional CPF mapping. And the intermediate frames are generated by the bidirectional interpolation for non-occlusion pixels combined with unidirectional projection for the occlusion pixels. Experimental results show that ORCMI improves the visual quality of the interpolated frames, especially at the occlusion regions. Compared to the block matching based FRUC algorithm, ORCMI can achieve 1∼2 dB PSNR gain for standard video sequences.

System identification in noisy data environments: An application to six Asian stock markets

This paper analyzes the systematic relationship between the stock market valuations, the nominal GDPs and the interest rates of six Asian countries, using not “single equation regression”, but an alternative methodology based on complete, multidirectional, least squares projections in the tradition of Frisch (1934). We compare the results with the spectral analysis of the information matrices and determine the noise levels. The objective is to extract the multidimensional economic system structures from the noisy empirical observations. This complete methodology sharply contrasts with the incomplete methodology of Fama [Fama, E.F., 1990. Stock returns, expected returns, and real activity. Journal of Finance 45, 1089–1108] and Schwert [Schwert, G.W., 1990. Stock returns and real activity: A century of evidence. Journal of Finance 45, 1237–1257], etc., who presume planal relations, fit them to the multidimensional data by only one prejudiced unidirectional projection, thereby ignoring between 75% and 92% of the available covariance information and not publishing all possible model projections. The results in this paper show that the analyzed countries are better analyzed using such complete multidirectional LS projections, even though the analysis is combinatorially much more complex. All six Asian financial-economic systems are high data noise environments, in which it is very difficult to separate the systematic signals from the noise. Because of these high noise levels, spectral analysis is very unreliable. We identify Taiwan’s stock market, economy and financial market to be rationally coherent. In contrast, Malaysia, Singapore, Philippines and Indonesia show only partially coherent systems, while no coherent system can be identified among Japan’s data.

System Identification in Noisy Data Environments: An Application to Six Asian Stock Markets

This paper analyzes the systematic relationship between the stock market valuations, the nominal GDPs and the interest rates of six Asian countries, using not single equation regession, but an alternative methodology based on complete, multidirectional, least squares projections. We compare the results with the spectral analysis of the information matices and determine the noise levels. The objective is to extract the multidimensional economic system structures from the noisy empirical observations. This complete methodology sharply contrasts with that of the incomplete methodology of Fama (1990), Schwert (1990), etc., who presume planal relations, fit them to the multidimensional data by only one prejudiced unidirectional projection, thereby ignoring between 75% - 92% of the available covariance information and not publishing the absolute majoity of all possible model projections. The results in this paper show that the analyzed countries are better analyzed using such complete multidirectional LS projections, even though the analysis is combinatorially much more complex. All six Asian financial-economic systems are high data noise environments, in which it is very difficult to separate the systematic signals from the noise. Because of these high noise levels, spectral analysis is very unreliable. We identify Taiwan's stock market, economy and financial market to be rationally coherent. In contrast, Malaysia, Singapore, Philippines and Indonesia show only partially coherent systems, while no coherent system can be identified among Japan's data.

Model Uncertainty, Complexity and Rank in Finance

There are three crucial mathematical system concepts in Finance, which are either being confused or misapplied - uncertainty, complexity and rank. First, the concept of epistemic uncertainty is sufficient for modeling and the concept of probability is unnecessary. This is illustrated by Galton's Error, and the under-repesentation of systematic risk by American mutual funds. These funds use simple unidirectional projection (regression) to compute Sharpe's beta for fund selection. There are at least five equivalent ways of representing the measured model uncertainty and a new and an improved risk categorization for mutual funds is presented. Second, the concept of (linear) system complexity is usually dealt with by presuming a model rank, as the Cowles Foundation erroneously prescribed in the early 1950s, and superimposing that model rank on the data, when a model is estimated. But the model rank does not have to be presumed: it can be identified from the data and all corresponding (Grassmanian) coefficients can be computed by CLS Projections. This is illustrated by the identification of the model rank of simple financial risk systems in six Asian countries, in particular in Taiwan. Third, often it is thought that Markowitz' portfolio optimization and exact and complete cash flow accounting are incompatible because of the non-existence, or empirical instability of the information matrix. The problem is caused by the rank constraints imposed by the portfolio accounting identities. But these rank constraints also provide the solution, since they form exact selectors of the portfolio allocations, which are found by simple tensor algebra. This will be illustrated by the optimization of an Asian multi-currency stock investment portfolio.

The Analytic Incompleteness of Unidirectional Projections or Regressions

Why do statisticians (econometricians, economists, financial analysts, etc.) continue to incompletely identify the algebraic/geometric structure of the multi-variate data series they profess to analyze, and instead continue to publish the results of incomplete, prejudiced and biased unidirectional projections (= regressions) of such covariance structures? Such incomplete, and biased representations cannot lead to scientific knowledge, as has already been demonstrated more than twenty years ago. This paper provides exact measurements of the resulting percentage of analytic incompletenss and of the number of conventionally ignored, but possible, LS projections, given a particular multivariate data set.

Feedback Inhibition Defines Transverse Processing Modules in the Lateral Amygdala

The lateral amygdaloid (LA) nucleus is the main input station of the amygdala for sensory afferents. However, it is unclear how the lateral nucleus transforms these inputs, because its intrinsic connectivity is poorly understood. Here, we took advantage of the fact that glutamatergic neurons of the lateral nucleus send a primarily unidirectional projection to the basomedial nucleus. Consequently, it was possible to infer the targets of their intranuclear axons (projection cells vs inhibitory interneurons) by backfiring some projection neurons from the basomedial nucleus and analyzing evoked responses in other LA projection cells. Basomedial stimuli evoked markedly different synaptic responses depending on the orientation of the slices. In coronal slices (intact and decorticated), the prevalent response of LA neurons was an inhibition, regardless of the stimulation intensity. This inhibition was sensitive to GABA(A) and non-NMDA receptor antagonists, suggesting that it was mediated by the activation of GABAergic cells of the LA. In contrast, basomedial stimuli primarily evoked EPSPs in horizontal slices, regardless of the position of recorded neurons. In light of these findings, we conclude that the prevalent target of the intrinsic axon collaterals of projection cells depend on the rostrocaudal position of target neurons with respect to the parent cell body: inhibitory interneurons at rostrocaudal proximity versus other projection cells at a distance. Thus, feedback interneurons effectively divide the lateral nucleus in transverse processing modules that prevent runaway excitation within each module but allow intermixing of sensory information in the rostrocaudal plane.

The projection from hippocampal area CA1 to the subiculum sustains long-term potentiation.

Long-term potentiation (LTP) is a popular model of the synaptic plasticity which may be engaged by the biological processes underlying learning and memory. Most available studies of LTP have concentrated on the analysis of LTP occurring in 'early' components of the hippocampal circuit (for example, dentate gyrus and area CA1). We examine here, for the first time, LTP as it occurs in the massive, unidirectional projection from CA1 to the subiculum in vivo. We show that this projection sustains high-frequency stimulus-induced LTP (10 trains of 20 stimuli at 200 Hz; intertrain interval 2 s; LTP 181 +/- 9% at 30 min post-LTP induction). In addition, input-output (I/O) curves show a leftward shift for all stimulation values.