Quantum control of chemical reaction dynamics in a classical way

科研費報告書収録論文(課題番号:10640480・基盤研究(C)(2)・H10～H12/研究代表者:藤村, 勇一/多自由度系化学反応ダイナミクスの量子制御

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures

Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science. However, since the (metric) space of persistence diagrams is not Hilbert, they end up being difficult inputs for most Machine Learning techniques. To address this concern, several vectorization methods have been put forward that embed persistence diagrams into either finite-dimensional Euclidean space or (implicit) infinite dimensional Hilbert space with kernels. In this work, we focus on persistence diagrams built on top of graphs. Relying on extended persistence theory and the so-called heat kernel signature, we show how graphs can be encoded by (extended) persistence diagrams in a provably stable way. We then propose a general and versatile framework for learning vectorizations of persistence diagrams, which encompasses most of the vectorization techniques used in the literature. We finally showcase the experimental strength of our setup by achieving competitive scores on classification tasks on real-life graph datasets

DTM-based Filtrations

Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Cech or Vietoris-Rips filtrations, are very sensitive to the presence of outliers in the data from which they are computed. In this paper, we introduce and study a new family of filtrations, the DTM-filtrations, built on top of point clouds in the Euclidean space which are more robust to noise and outliers. The approach adopted in this work relies on the notion of distance-to-measure functions, and extends some previous work on the approximation of such functions.Comment: Abel Symposia, Springer, In press, Topological Data Analysi

The Influence of Hyperactivity of the Hypothalamic-pituitary-adrenal Axis and Hyperglycemia on the 5-HT2A Receptor-mediated Wet-dog Shake Responses in Rats

Hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis induces hyperglycemia and serotonin (5-HT)2A receptor supersensitivity. In the present study, to investigate the effect of hyperglycemia on the function of 5-HT2A receptors, we compared the 5-HT2A receptor-mediated wet-dog shake responses in rats treated with adrenocorticotropic hormone (ACTH), dexamethasone and streptozotocin. ACTH (100 &#956;g/rat per day, s.c.), dexamethasone (1 mg/kg per day, s.c.) and streptozotocin (60 mg/kg, i.p.) produced significant hyperglycemia at 14 days after the start of these treatments, and the hyperglycemia was most pronounced in the streptozotocin-treated rats. The wet-dog shake responses induced by (±)-1-(2,5-dimethoxy-4-iodophenyl)-2-aminopropane (DOI), a 5-HT2A receptor agonist, were significantly enhanced at 14 days after repeated treatment with ACTH and dexamethasone. However, streptozotocin-induced diabetes had no effect on the wet-dog shake responses. The results of the present study suggest that hyperglycemia is not strongly associated with the enhanced susceptibility of 5-HT2A receptors under the condition of hyperactivity of the HPA axis.</p

DTM-Based Filtrations

Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Cech or Vietoris-Rips filtrations, are very sensitive to the presence of outliers in the data from which they are computed. In this paper, we introduce and study a new family of filtrations, the DTM-filtrations, built on top of point clouds in the Euclidean space which are more robust to noise and outliers. The approach adopted in this work relies on the notion of distance-to-measure functions and extends some previous work on the approximation of such functions

ATOL: Measure Vectorisation for Automatic Topologically-Oriented Learning

Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning frameworks. We introduce a learnt, unsupervised measure vectorisation method and use it for reflecting underlying changes in topological behaviour in machine learning contexts. Relying on optimal measure quantisation results the method is tailored to efficiently discriminate important plane regions where meaningful differences arise. We showcase the strength and robustness of our approach on a number of applications, from emulous and modern graph collections where the method reaches state-of-the-art performance to a geometric synthetic dynamical orbits problem. The proposed methodology comes with only high level tuning parameters such as the total measure encoding budget, and we provide a completely open access software

Spontaneous Activation of Event Details in Episodic Future Simulation

Episodic future simulation is supported by both the retrieval and recombination of episodic details. It remains unclear, however, how individuals retrieve episodic details from memory to construct possible future scenarios; for this people must use details related to the planned future events appropriately. A potentially relevant cognitive process is the spontaneous activation of intention observed in prospective memory (i.e., the intention superiority effect). Previous studies on prospective memory have shown that the approximation of retrieval opportunities for future intentions activate related information, suggesting that the intention superiority effect is context-sensitive. We hypothesized that the same cognitive process underlies future simulation—that is, details related to future events should spontaneously become activated at the appropriate moment of future simulation to make that simulation plausible. In Experiment 1, participants took part in future experiments and formed intentions to perform particular actions for the next experiments. Subsequently, they imagined events that could occur up until they arrived at the experimental room on the day of the next experiment. During this exercise, they did not imagine engaging in the required experimental task. We measured the conceptual activation of intention-related information via a recognition task using intended action words as targets. The results showed the intention superiority effect—concepts related to participants’ future intentions became active when envisioning future events approaching the next experiment. In Experiments 2 and 3, we ensured that the intention superiority effect in future simulation was context-sensitive by adding a control condition that required participants to imagine events other than the approaching future experiments. These results indicated that concepts related to the intended actions were spontaneously activated when imagined future events became both temporally and spatially close to the future simulation. Our finding suggests that spontaneous activation of details approaching the context of a future simulation helps in constructing plausible future scenarios

Effects of Physical and Psychological Stress on 5-HT2A Receptor-mediated Wet-dog Shake Responses in Streptozotocin-induced Diabetic Rats.

Several epidemiological and clinical studies have indicated that the prevalence of psychiatric disorders is higher in diabetic patients than in the general population. In the present studies, we examined the behavioral changes in streptozotocin-induced diabetic rats, and investigated the effects of physical and psychological stress on the hippocampal BDNF levels and on the serotonin 2A (5-HT2A) receptor-mediated wet-dog shake responses. The streptozotocin (60 mg/kg, i.p.)-induced diabetes had no significant effects on the immobility time in the forced swim test or on locomotor activity in the open-field test. Moreover, there was no significant difference in the wet-dog shake responses induced by DOI, a 5-HT2A receptor agonist, between nondiabetic and diabetic rats. Five-day exposure to physical (electric footshock) and psychological (non-footshock) stress had no signifi cant effect on the hippocampal BDNF level in diabetic or nondiabetic rats. The 2 types of stress had no significant effect on the DOI-induced wet-dog shake responses in nondiabetic rats. In diabetic rats, the repeated exposure to physical stress markedly increased the DOI-induced wet-dog shake responses, but the repeated exposure to psychological stress had no effect. These results suggest that exposure to physical stress augmented the susceptibility to the wet-dog shake responses to 5-HT2A receptor stimulation in streptozotocin-induced diabetic rats

JWST Measurements of Neutral Hydrogen Fractions and Ionized Bubble Sizes at z=7−12z=7-12 Obtained with Lyα\alpha Damping Wing Absorptions in 26 Bright Continuum Galaxies

We present volume-averaged neutral hydrogen fractions x_{\rm \HI} and ionized bubble radii $R_{\rm b}$ measured with Ly$\alpha$ damping wing absorptions of galaxies at the epoch of reionization. We combine JWST/NIRSpec spectra taken by CEERS, GO-1433, and DDT-2750 programs, and obtain 26 bright UV-continuum galaxies at $7<z<12$. We construct 4 composite spectra binned by redshift, and find the clear evolution of spectral flattening towards high redshift at the rest-frame $1216$ \AA\ suggesting the increase of Ly$\alpha$ damping wing absorption. We estimate Ly$\alpha$ damping wing absorption in the composite spectra with realistic templates including Ly$\alpha$ emission and circum-galactic medium absorptions. Assuming the standard inside-out reionization picture having an ionized bubble with $R_{\rm b}$ around a galaxy in the inter-galactic medium of x_{\rm \HI}, we obtain x_{\rm \HI} ($R_{\rm b}$) values monotonically increasing (decreasing) from x_{\rm \HI}={0.46}^{+0.36}_{-0.32} to ${0.83}^{+0.12}_{-0.21}$ ($R_{\rm b}={1.49}^{+0.37}_{-0.43}\times10^2$ to ${5.04}^{+8.06}_{-3.73}$ comoving Mpc) at redshift $7.140^{+0.039}_{-0.076}$ to $9.801^{+1.599}_{-1.164}$. The redshift evolution of x_{\rm \HI} indicates moderately late reionization history consistent with the one suggested from the electron scattering of cosmic microwave background and the evolution of UV luminosity function with an escape fraction $f_{\rm esc}\simeq 0.17$. Our $R_{\rm b}$ measurements are about 20 times larger than the cosmic average values estimated by analytic calculations for a given x_{\rm \HI}, while our $R_{\rm b}$ measurements are comparable with the values for merged ionized bubbles around bright galaxies predicted by recent numerical simulations