Chandra Observation of a Weak Shock in the Galaxy Cluster A2556

Based on a 21.5 ks \chandra\ observation of A2556, we identify an edge on the surface brightness profile (SBP) at about 160$h_{71}^{-1}$ kpc northeast of the cluster center, and it corresponds to a shock front whose Mach number $\mathcal{M}$ is calculated to be $1.25_{-0.03}^{+0.02}$. No prominent substructure, such as sub-cluster, is found in either optical or X-ray band that can be associated with the edge, suggesting that the conventional super-sonic motion mechanism may not work in this case. As an alternative solution, we propose that the nonlinear steepening of acoustic wave, which is induced by the turbulence of the ICM at the core of the cluster, can be used to explain the origin of the shock front. Although nonlinear steepening weak shock is expected to occur frequently in clusters, why it is rarely observed still remains a question that requires further investigation, including both deeper X-ray observation and extensive theoretical studies.Comment: 15 pages, 4 figures, accepted by Ap

Self-referentiality in Constructive Semantics of Intuitionistic and Modal Logics

This thesis explores self-referentiality in the framework of justification logic. In this framework initialed by Artemov, the language has formulas of the form t:F, which means the term t is a justification of the formula F. Moreover, terms can occur inside formulas and hence it is legal to have t:F(t), which means the term t is a justification of the formula F about t itself. Expressions like this is not only interesting in the semantics of justification logic, but also, as we will see, necessary in applications of justification logic in formalizing constructive contents implicitly carried by modal and intuitionistic logics. Works initialed by Artemov and followed by Brezhnev and others have successfully extracted constructive contents packaged by modality in many modal logics. Roughly speaking, they offer methods of substituting modalities by terms in various justification logics, and then computing the exact structure of each term. After performing these methods, each formula prefixed by a modality becomes a formula prefixed by a term, which intuitively stands for the justification of the formula being prefixed. In terminology of this framework, we say that modal logics are realized in justification logics. Within the family of justification logics, the Logic of Proofs LP is perhaps the most important member. As Artemov showed, this logic is not only complete w.r.t. to arithmetical semantics about proofs, but also accommodates the modal logic S4 via realization. Combined with Godel\u27s modal embedding from intuitionistic propositional logic IPC to S4, the Logic of Proofs LP serves as an intermedium via which IPC receives its provability semantics, also known as Brouwer-Heyting-Kolmogorov semantics, or BHK semantics. This thesis presents the candidate\u27s works in two directions. (1) Following Kuznets\u27result that self-referentiality is necessary for the realization of several modal logics including S4, we show that it is also necessary for BHK semantics. (2) We find a necessary condition for a modal theorem to require self-referentiality in its realization, and using this condition to derive many interesting properties about self-referentiality

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders.

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging

Extremal permutations in routing cycles

Let G be a graph whose vertices are labeled 1, ... , n, and pi be a permutation on [n] := {1, 2, ... , n}. A pebble p(i) that is initially placed at the vertex i has destination pi(i) for each i is an element of [n]. At each step, we choose a matching and swap the two pebbles on each of the edges. Let rt(G, pi), the routing number for pi, be the minimum number of steps necessary for the pebbles to reach their destinations. Li, Lu and Yang proved that rt(C-n, pi) = 5, if rt(C-n, pi) = n-1, then pi = 23 ... n1 or its inverse. By a computer search, they showed that the conjecture holds for n \u3c 8. We prove in this paper that the conjecture holds for all even n \u3e= 6

Feature Fusion-Based Capsule Network for Cross-Subject Mental Workload Classification

In a complex human-computer interaction system, estimating mental workload based on electroencephalogram (EEG) plays a vital role in the system adaption in accordance with users’ mental state. Compared to within-subject classification, cross-subject classification is more challenging due to larger variation across subjects. In this paper, we targeted the cross-subject mental workload classification and attempted to improve the performance. A capsule network capturing structural relationships between features of power spectral density and brain connectivity was proposed. The comparison results showed that it achieved a cross-subject classification accuracy of 45.11%, which was superior to the compared methods (e.g., convolutional neural network and support vector machine). The results also demonstrated feature fusion positively contributed to the cross-subject workload classification. Our study could benefit the future development of a real-time workload detection system unspecific to subjects

Exploring the Cosmic Reionization Epoch in Frequency Space: An Improved Approach to Remove the Foreground in 21 cm Tomography

Aiming to correctly restore the redshifted 21 cm signals emitted by the neutral hydrogen during the cosmic reionization processes, we re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they works satisfactorily with complex foreground, by quantitatively evaluate the quality of restored 21 cm signals in terms of sample statistics. We construct the foreground model to characterize both spatial and spectral substructures of the real sky, and use it to simulate the observed radio spectra. By comparing between different separation approaches through statistical analysis of restored 21 cm spectra and corresponding power spectra, as well as their constraints on the mean halo bias $b$ and average ionization fraction $x_e$ of the reionization processes, at $z=8$ and the noise level of 60 mK we find that, although the complex foreground can be well approximated with quadratic polynomial expansion, a significant part of Mpc-scale components of the 21 cm signals (75% for $\gtrsim 6h^{-1}$ Mpc scales and 34% for $\gtrsim 1h^{-1}$ Mpc scales) is lost because it tends to be mis-identified as part of the foreground when single-narrow-segment separation approach is applied. The best restoration of the 21 cm signals and the tightest determination of $b$ and $x_e$ can be obtained with the three-narrow-segment fitting technique as proposed in this paper. Similar results can be obtained at other redshifts.Comment: 33 pages, 14 figures. Accepted for publication in Ap

The Effect of Longitudinal Training on Working Memory Capacities: An Exploratory EEG Study

The study of working memory (WM) is a hot topic in recent years and accumulating literatures underlying the achievement and neural mechanism of WM. However, the effect of WM training on cognitive functions were rarely studied. In this study, nineteen healthy young subjects participated in a longitudinal design with one week N-back training (N=1,2,3,4). Experimental results demonstrated that training procedure could help the subjects master more complex psychological tasks when comparing the pre-training performance with those post-training. More specifically, the behavior accuracy increased from 68.14±9.34%, 45.09±14.90%, 39.12±12.71%, and 32.11±10.98% for 1-back, 2-back, 3-back and 4-back respectively to 73.52±4.01%, 69.14±5.28%, 69.09±6.41% and 64.41±5.12% after training. Furthermore, we applied elec-troencephalogram (EEG) power and functional connectivity to reveal the neural mechanisms of this beneficial effect and found that the EEG power of δ, θ and α band located in the left temporal and occipital lobe increased significantly. Meanwhile, the functional connectivity strength also increased obviously in δ and θ band. In sum, we showed positive effect of WM training on psychological performance and explored the neural mechanisms. Our findings may have the implications for enhancing the performance of participants who are prone to cognitive

Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition

In recent years, a great deal of attention has been paid to the Transformer network for speech recognition tasks due to its excellent model performance. However, the Transformer network always involves heavy computation and large number of parameters, causing serious deployment problems in devices with limited computation sources or storage memory. In this paper, a new lightweight model called Sim-T has been proposed to expand the generality of the Transformer model. Under the help of the newly developed multiplexing technique, the Sim-T can efficiently compress the model with negligible sacrifice on its performance. To be more precise, the proposed technique includes two parts, that are, module weight multiplexing and attention score multiplexing. Moreover, a novel decoder structure has been proposed to facilitate the attention score multiplexing. Extensive experiments have been conducted to validate the effectiveness of Sim-T. In Aishell-1 dataset, when the proposed Sim-T is 48% parameter less than the baseline Transformer, 0.4% CER improvement can be obtained. Alternatively, 69% parameter reduction can be achieved if the Sim-T gives the same performance as the baseline Transformer. With regard to the HKUST and WSJ eval92 datasets, CER and WER will be improved by 0.3% and 0.2%, respectively, when parameters in Sim-T are 40% less than the baseline Transformer