Atomic Dipole Squeezing in the Correlated Two-Mode Two-Photon Jaynes-Cummings Model

In this paper, we study the atomic dipole squeezing in the correlated two-mode two-photon JC model with the field initially in the correlated two-mode SU(1,1) coherent state. The effects of detuning, field intensity and number difference between the two field modes are investigated through numerical calculation

Metabolite Mapping with Extended Brain Coverage Using a Fast Multisection MRSI Pulse Sequence and a Multichannel Coil

Multisection magnetic resonance spectroscopic imaging is a widely used pulse sequence that has distinct advantages over other spectroscopic imaging sequences, such as dynamic shimming, large region-of-interest coverage within slices, and rapid data acquisition. It has limitations, however, in the number of slices that can be acquired in realistic scan times and information loss from spacing between slices. In this paper, we synergize the multi-section spectroscopic imaging pulse sequence with multichannel coil technology to overcome these limitations. These combined techniques now permit elimination of the gaps between slices and acquisition of a larger number of slices to realize the whole brain metabolite mapping without incurring the penalties of longer repetition times (and therefore longer acquisition times) or lower signal-to-noise ratios

Binary Structuring Elements Decomposition Based on an Improved Recursive Dilation-Union Model and RSAPSO Method

This paper proposed an improved approach to decompose structuring elements of an arbitrary shape. For the model of this method, we use an improved dilation-union model, adding a new termination criterion, as the sum of 3-by-3 matrix should be less than 5. Next for the algorithm of this method, we introduced in the restarted simulated annealing particle swarm optimization method. The experiments demonstrate that our method can find better results than Park's method, Anelli's method, Shih's SGA method, and Zhang's MFSGA method. Besides, our method gave the best decomposition tree of different SE shapes including “ship,” “car,” “heart,” “umbrella,” “vase,” “tree,” “cat,” “V,” “bomb,” and “cup.

Energy Preserved Sampling for Compressed Sensing MRI

The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time

Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce \emph{neighborhood cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches.Comment: Accepted by AAAI2020 with oral presentation (https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf). Since AAAI2020 has started, I have the right to distribute this paper on arXi

Comment on “An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps”

This paper researched three definitions of Gauss map and found that the definition of “Gauss map” in the paper of Arasomwan and Adewumi may be incoherent with other publications. In addition, we analyzed the difference of continuous Gauss map and the floating-point Gauss map, and we pointed out that the floating-point simulation behaved significantly differently from the continuous Gauss map

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper.Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform(FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 x K-fold stratified cross validation test showed that this proposed “FRFE +WTT + TSVM” yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed “FRFE +WTT + TSVM” method is superior to 20 state-of-the-art methods

Effects of Davunetide on N-acetylaspartate and Choline in Dorsolateral Prefrontal Cortex in Patients with Schizophrenia

Schizophrenia is associated with extensive neurocognitive and behavioral impairments. Studies indicate that N-acetylaspartate (NAA), a marker of neuronal integrity, and choline, a marker of cell membrane turnover and white matter integrity, may be altered in schizophrenia. Davunetide is a neurotrophic peptide that can enhance cognitive function in animal models of neurodegeneration. Davunetide has recently demonstrated modest functional improvement in a study of people with schizophrenia. In a subset of these subjects, proton magnetic resonance spectroscopy (1H-MRS) was conducted to explore the effects of davunetide on change in NAA/creatine (NAA/Cr) and choline/creatine (choline/Cr) over 12 weeks of treatment. Of 63 outpatients with schizophrenia who received randomized davunetide (5 and 30 mg/day) or placebo in the parent clinical trial, 18 successfully completed 1H-MRS in dorsolateral prefrontal cortex (DLPFC) at baseline and at 12 weeks. Cognition was assessed using the MATRICS Consensus Cognitive Battery (MCCB). NAA/Cr was unchanged for combined high- and low-dose davunetide groups (N=11). NAA/Cr in the high-dose davunetide group (N=8) suggested a trend increase of 8.0% (P=0.072) over placebo (N=7). Choline/Cr for combined high- and low-dose davunetide groups suggested a 6.4% increase (P=0.069), while the high-dose group showed a 7.9% increase (P=0.040) over placebo. Baseline NAA/Cr correlated with the composite MCCB score (R=0.52, P=0.033), as did individual cognitive domains of attention/vigilance, verbal learning, and social cognition; however, neither metabolite correlated with functional capacity. In this exploratory study, 12 weeks of adjunctive davunetide appeared to produce modest increases in NAA/Cr and choline/Cr in DLPFC in people with schizophrenia. This is consistent with a potential neuroprotective mechanism for davunetide. The data also support use of MRS as a useful biomarker of baseline cognitive function in schizophrenia. Future clinical and preclinical studies are needed to fully define the mechanism of action and cognitive effects of davunetide in schizophrenia