A quantum hydrodynamical description for scrambling and many-body chaos

Recent studies of out-of-time ordered thermal correlation functions (OTOC) in holographic systems and in solvable models such as the Sachdev-Ye-Kitaev (SYK) model have yielded new insights into manifestations of many-body chaos. So far the chaotic behavior has been obtained through explicit calculations in specific models. In this paper we propose a unified description of the exponential growth and ballistic butterfly spreading of OTOCs across different systems using a newly formulated "quantum hydrodynamics," which is valid at finite $\hbar$ and to all orders in derivatives. The scrambling of a generic few-body operator in a chaotic system is described as building up a "hydrodynamic cloud," and the exponential growth of the cloud arises from a shift symmetry of the hydrodynamic action. The shift symmetry also shields correlation functions of the energy density and flux, and time ordered correlation functions of generic operators from exponential growth, while leads to chaotic behavior in OTOCs. The theory also predicts an interesting phenomenon of the skipping of a pole at special values of complex frequency and momentum in two-point functions of energy density and flux. This pole-skipping phenomenon may be considered as a "smoking gun" for the hydrodynamic origin of the chaotic mode. We also discuss the possibility that such a hydrodynamic description could be a hallmark of maximally chaotic systems.Comment: 48 pages, 9 figures. v2: references added, various clarifications made including an expanded discussion of predictions in the introduction and an expanded discussion of four-point functions, v3: journal versio

A Monolithically Fabricated Combinatorial Mixer for Microchip-Based High-Throughput Cell Culturing Assays

We present an integrated method to fabricate 3- D microfluidic networks and fabricated the first on-chip cell culture device with an integrated combinatorial mixer. The combinatorial mixer is designed for screening the combinatorial effects of different compounds on cells. The monolithic fabrication method with parylene C as the basic structural material allows us to avoid wafer bonding and achieves precise alignment between microfluidic channels. As a proof-of-concept, we fabricated a device with a three-input combinatorial mixer and demonstrated that the mixer can produce all the possible combinations. Also, we demonstrated the ability to culture cells on-chip and performed a simple cell assay on-chip using trypan blue to stain dead cells

Discovering student interactions with a collaborative learning forum that predict group collaboration problems

This paper investigates the role of various student interactions with a learning forum in order to ascertain the existence of different group collaboration problems. A particular focus of interest has been learning forums, since forums have become broadly adopted tools to support online group collaboration. The types of collaboration problems were drawn from previous research that identified the main student-induced collaboration problems. A data set was collected for 87 undergraduates who participated in a web-based computer science group project. It consists of two kinds of data. The first is student interaction data which were collected from a learning forum system on which the group project was undertaken. The second is the data relating to assessment of group collaboration problems, and were gathered through a questionnaire delivered to the students who participated in the group project. Multinomial logistic regression analysis has been applied for modelling the relationship between a response variable corresponding to the existence of a group collaboration problem and several predictor variables representing various student interactions with a learning forum. A set of predictive models were produced by the regression analysis, each representing a statistically significant combination of student interactions that predict the existence of one of the collaboration problems in question. The findings reveal that indicators including the number of posts that were created and replied to by individual students, and the number of times that a student viewed a discussion on a learning forum, contribute significantly in predicting the collaboration problems which were identified. The results also demonstrate that how the existence of a problem fluctuates with the alterations in the value of an indicator variable. The goodness-of-fit of the identified predictive models was measured by the Pearson chi-square test and the results of this test indicate that the models fit the sample data well. The average rate of correct classification by the models was approximately 80%, which means the regression method performs well on the sample data set. The outcomes of this research can help teachers to assess problems in web-based collaborative group work and also can be used to develop tools for automatically diagnosing group collaboration problems in web-based collaborative learning environments

H\u3csup\u3e+\u3c/sup\u3e- and Na\u3csup\u3e+\u3c/sup\u3e- elicited rapid changes of the microtubule cytoskeleton in the biflagellated green alga \u3cem\u3eChlamydomonas\u3c/em\u3e

Although microtubules are known for dynamic instability, the dynamicity is considered to be tightly controlled to support a variety of cellular processes. Yet diverse evidence suggests that this is not applicable to Chlamydomonas, a biflagellate fresh water green alga, but intense autofluorescence from photosynthesis pigments has hindered the investigation. By expressing a bright fluorescent reporter protein at the endogenous level, we demonstrate in real time discreet sweeping changes in algal microtubules elicited by rises of intracellular H+ and Na+. These results from this model organism with characteristics of animal and plant cells provide novel explanations regarding how pH may drive cellular processes; how plants may respond to, and perhaps sense stresses; and how organisms with a similar sensitive cytoskeleton may be susceptible to environmental changes

MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics

Background Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method. Results We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy. Conclusions We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model

Incorporating learning styles in a computer-supported collaborative learning model

Collaborative learning enables individual learners to combine their own expertise, experience and ability to accomplish a mutual learning goal. The grouping of learners, and learning from social interactions with peer-learners, are two basic characteristics of collaborative learning. For individual learners to benefit from collaborative learning, individual learners with different characteristics must be grouped together. In this paper, we propose a computer-supported collaborative learning model which incorporates learning styles for improving collaborative learning. The proposed model is novel since it can provide overall support for collaborative learning. In addition, the way we have incorporated learning styles in the model is a new approach to constituting heterogeneous groups containing learners with dissimilar learning styles and detect learning styles through monitoring collaborative interactions