MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space

Eye-tracking research has proven valuable in understanding numerous cognitive functions. Recently, Frey et al. provided an exciting deep learning method for learning eye movements from fMRI data. However, it needed to co-register fMRI into standard space to obtain eyeballs masks, and thus required additional templates and was time consuming. To resolve this issue, in this paper, we propose a framework named MRGazer for predicting eye gaze points from fMRI in individual space. The MRGazer consisted of eyeballs extraction module and a residual network-based eye gaze prediction. Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol and achieves end-to-end eye gaze regression. The proposed method achieved superior performance in a variety of eye movement tasks than the co-registration-based method, and delivered objective results within a shorter time (~ 0.02 Seconds for each volume) than prior method (~0.3 Seconds for each volume)

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an $\alpha$-$\beta$-$\gamma$ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an $\alpha$ process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a $\beta$ process updating the social relations based on related attributes, and (iii) a $\gamma$ process updating individual's attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.Comment: Long paper (oral) accepted by ACL-IJCNLP 202

(6aS,11aR,11cS)-8-Sulfanylidene-2,3,5,6,6a,7,11,11a,11b,11c-decahydro-3a,7a-diaza-1H,4H-benzo[de]anthracen-3a-ium chloride hemihydrate

The title compound, C15H23N2S+·Cl−·0.5H2O, was prepared from (6aS,11aR,11cS)-2,3,5,6,6a,7,11,11a,11b,11c-deca­hydro-3a,7a-diaza-1H,4H-benzo[de]anthracene-8-one (sophocarpine) and Lawesson’s reagent. The thione-substituted ring is in an envelope conformation and the three other six-membered rings are in chair conformations. In the crystal, anions and cations are linked by N—H⋯Cl and weak C—H⋯Cl hydrogen bonds. One 0.5-occupancy solvent water mol­ecule lies on a twofold rotation axis and another 0.25-occupancy solvent water mol­ecule is in a general position. The H atoms of these water mol­ecules were not located or included in the refinement

Preparation of activated carbon from a renewable agricultural residue of pruning mulberry shoot

In this study, element composition of pruning mulberry shoot was evaluated by element analysis. On the basis of this, pruning mulberry shoot–based activated carbon was prepared from by chemical activationwith phosphoric acid (H3PO4), which particles were displayed by the scanning electron microscope (SEM) micrographs. The influence of impregnation temperature, impregnation ratio, H3PO4 concentration,pyrolysis temperature, and pyrolysis time on the iodine adsorption capacity and yield of the prepared activated carbon were investigated and discussed. Results showed that, pruning mulberry shoot is a good and cheap agricultural residue for the production of activated carbon, with carbon, hydrogen and nitrogen contents of 44.58, 6.37 and 1.45% (w/w, dry basis), respectively. With an impregnation temperature of 80°C, an impregnation ratio of 2:1 (v/w), a H3PO4 concentration of 50%, an pyrolysis temperature of 500°C, and an pyrolysis time of 2 h, the activated carbon with better iodine adsorption capacity and yield were 887.35 mg/g and 38.12%, respectively. SEM experimental results indicated thepotential use of pruning mulberry shoots as a precursor in the activated carbon preparation process, thus, representing an economically promising material

Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)

The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.Comment: 13 pages, 6 figure

Three-dimensional resolution-enhancement divided aperture correlation-differential confocal microscopy with nanometer axial focusing capability

Divided aperture confocal microscopy (DACM) provides an improved imaging depth, imaging contrast, and working distance at the expense of spatial resolution. Here, we present a new method-divided aperture correlation-differential confocal microscopy (DACDCM) to improve the DACM resolution and the focusing capability, without changing the DACM configuration. DACDCM divides the DACM image spot into two round regions symmetrical about the optical axis. Then the light intensity signals received simultaneously from two round regions by a charge-coupled device (CCD) are processed by correlation manipulation and differential subtraction to improve the DACM spatial resolution and axial focusing capability, respectively. Theoretical analysis and preliminary experiments indicate that, for the excitation wavelength of λ = 632.8 nm, numerical aperture NA = 0.8, and normalized offset vM = 3.2 of the two regions, the DACDCM resolution is improved by 32.5% and 43.1% in the x and z directions, simultaneously, compared with that of the DACM. The axial focusing resolution used for the sample surface profile imaging was also significantly improved to 2 nm

Theoretical and Experimental Demonstration on Grating Lobes of Liquid Crystal Optical Phased Array

High deflection efficiency is one of the urgent requirements for practical liquid crystal optical phased array (LC-OPA). In this paper, we demonstrate that high order grating lobes induced from fringe effect are the most important issue to reduce occupation of main lobe. A novel theoretical model is developed to analyze the feature of grating lobes when the device of LC-OPA is working on the scheme of variable period grating (VPG) or variable blazing grating (VBG). Subsequently, our experiments present the relevant results showing a good agreement with the theoretical analysis

The role of microRNA-1246 in the regulation of B cell activation and the pathogenesis of systemic lupus erythematosus

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