A natural language fMRI dataset for voxelwise encoding models.

Speech comprehension is a complex process that draws on humans' abilities to extract lexical information, parse syntax, and form semantic understanding. These sub-processes have traditionally been studied using separate neuroimaging experiments that attempt to isolate specific effects of interest. More recently it has become possible to study all stages of language comprehension in a single neuroimaging experiment using narrative natural language stimuli. The resulting data are richly varied at every level, enabling analyses that can probe everything from spectral representations to high-level representations of semantic meaning. We provide a dataset containing BOLD fMRI responses recorded while 8 participants each listened to 27 complete, natural, narrative stories (~6 hours). This dataset includes pre-processed and raw MRIs, as well as hand-constructed 3D cortical surfaces for each participant. To address the challenges of analyzing naturalistic data, this dataset is accompanied by a python library containing basic code for creating voxelwise encoding models. Altogether, this dataset provides a large and novel resource for understanding speech and language processing in the human brain.

Entropic Dynamic Time Warping Kernels for Co-Evolving Financial Time Series Analysis.

Network representations are powerful tools to modeling the dynamic time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices. In this work, we develop a novel framework to compute the kernel-based similarity measure between dynamic time-varying financial networks. Specifically, we explore whether the proposed kernel can be employed to understand the structural evolution of the financial networks with time associated with standard kernel machines. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time (CT) matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the CT matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop an entropic dynamic time warping kernels through the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate that the effectiveness of the proposed method.

Supramolecular vesicles based on pillar[n]arenes: design, construction, and applications.

Supramolecular vesicles have attracted considerable attention due to their advantages of facile construction, high-cargo-loading capacity, and good biocompatibility. Pillar[n]arenes are a unique family of supramolecular macrocycles, exhibiting excellent features and broad applications due to their intrinsic topology and high functionality. In the past decade, the construction of pillar[n]arene-based supramolecular vesicles has been continuously attempted and developed rapidly. In this review, we mainly summarize the significant advancements of such supramolecular vesicles in the last three years. By showing some representative examples, the design strategies, construction methods, and potential applications of these dynamic nanocarriers are discussed in detail. In particular, the responsiveness of such vesicles to various external stimuli and their applications in drug delivery are highlighted. The outstanding performance of pillar[n]arene-based supramolecular vesicles would definitely enrich the family of supramolecular vesicles and promote the development of dynamic supramolecular materials.

Dynamic hydrogels mediated by macrocyclic host-guest interactions.

Hydrogels have attracted increasing research interest in recent years due to their dynamic properties and potential applications in biomaterials. Concurrently, macrocycle-based host-guest interactions have played an important role in the development of supramolecular chemistry. Recently, research towards dynamic hydrogels mediated by various macrocyclic host-guest interactions has been gradually disclosed. In this review, we will outline the burgeoning progress in the development of functional hydrogels mediated by various host molecules, such as cyclodextrins, cucurbit[n]urils, calix[n]arenes, pillar[n]arenes, and other macrocycles. Smart hydrogels with outstanding properties, like biocompatibility, toughness, and self-healing, are mainly focused. We believe that this review will highlight the potential of dynamic hydrogels mediated by macrocycle-based host-guest interactions.

Trion Valley Coherence in Monolayer Semiconductors.

The emerging field of valleytronics aims to exploit the valley pseudospin of electrons residing near Bloch band extrema as an information carrier. Recent experiments demonstrating optical generation and manipulation of exciton valley coherence (the superposition of electron-hole pairs at opposite valleys) in monolayer transition metal dichalcogenides (TMDs) provide a critical step towards control of this quantum degree of freedom. The charged exciton (trion) in TMDs is an intriguing alternative to the neutral exciton for control of valley pseudospin because of its long spontaneous recombination lifetime, its robust valley polarization, and its coupling to residual electronic spin. Trion valley coherence has however been unexplored due to experimental challenges in accessing it spectroscopically. In this work, we employ ultrafast two-dimensional coherent spectroscopy to resonantly generate and detect trion valley coherence in monolayer MoSe2 demonstrating that it persists for a few-hundred femtoseconds. We conclude that the underlying mechanisms limiting trion valley coherence are fundamentally different from those applicable to exciton valley coherence.

Neutral and charged inter-valley biexcitons in monolayer MoSe2.

In atomically thin transition metal dichalcogenides (TMDs), reduced dielectric screening of the Coulomb interaction leads to strongly correlated many-body states, including excitons and trions, that dominate the optical properties. Higher-order states, such as bound biexcitons, are possible but are difficult to identify unambiguously using linear optical spectroscopy methods. Here, we implement polarization-resolved two-dimensional coherent spectroscopy (2DCS) to unravel the complex optical response of monolayer MoSe2 and identify multiple higher-order correlated states. Decisive signatures of neutral and charged inter-valley biexcitons appear in cross-polarized two-dimensional spectra as distinct resonances with respective ∼20 and ∼5 meV binding energies-similar to recent calculations using variational and Monte Carlo methods. A theoretical model considering the valley-dependent optical selection rules reveals the quantum pathways that give rise to these states. Inter-valley biexcitons identified here, comprising of neutral and charged excitons from different valleys, offer new opportunities for developing ultrathin biexciton lasers and polarization-entangled photon sources.

Coherent and Incoherent Coupling Dynamics between Neutral and Charged Excitons in Monolayer MoSe2.

The optical properties of semiconducting transition metal dichalcogenides are dominated by both neutral excitons (electron-hole pairs) and charged excitons (trions) that are stable even at room temperature. While trions directly influence charge transport properties in optoelectronic devices, excitons may be relevant through exciton-trion coupling and conversion phenomena. In this work, we reveal the coherent and incoherent nature of exciton-trion coupling and the relevant time scales in monolayer MoSe2 using optical two-dimensional coherent spectroscopy. Coherent interaction between excitons and trions is definitively identified as quantum beating of cross peaks in the spectra that persists for a few hundred femtoseconds. For longer times up to 10 ps, surprisingly, the relative intensity of the cross peaks increases, which is attributed to incoherent energy transfer likely due to phonon-assisted up-conversion and down-conversion processes that are efficient even at cryogenic temperature.

Tailoring Plasmonic Enhanced Upconversion in Single NaYF4:Yb(3+)/Er(3+) Nanocrystals.

By using silver nanoplatelets with a widely tunable localized surface plasmon resonance (LSPR), and their corresponding local field enhancement, here we show large manipulation of plasmonic enhanced upconversion in NaYF4:Yb(3+)/Er(3+) nanocrystals at the single particle level. In particular, we show that when the plasmonic resonance of silver nanolplatelets is tuned to 656 nm, matching the emission wavelength, an upconversion enhancement factor ~5 is obtained. However, when the plasmonic resonance is tuned to 980 nm, matching the nanocrystal absorption wavelength, we achieve an enhancement factor of ~22 folds. The precise geometric arrangement between fluorescent nanoparticles and silver nanoplatelets allows us to make, for the first time, a comparative analysis between experimental results and numerical simulations, yielding a quantitative agreement at the single particle level. Such a comparison lays the foundations for a rational design of hybrid metal-fluorescent nanocrystals to harness the upconversion enhancement for biosensing and light harvesting applications.

[The application of radiological image in forensic medicine].

Personal identification is an important work in forensic investigation included sex discrimination, age and stature estimation. Human identification depended on radiological image technique analysis is a practice and proper method in forensic science field. This paper intended to understand the advantage and defect by reviewed the employing of forensic radiology in forensic science field broadly and provide a reference to perfect the application of forensic radiology in forensic science field.