Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-organized criticality for dendritic readiness potential

Self-organized criticality is a principle explaining avalanche-like phenomena obeying power-laws in integrate-and-fire type dynamical systems. Here, we demonstrate that the behaviorally relevant brain neurons, mediating voluntary and reflexive behaviors in crayfish, show signatures of self-organized criticality. The dendritic activities are into the critical states following power-laws; characterized by scaling functions within neurons; but mapped to the common scaling relation across individuals. The relation is in line with the extracellular neuronal avalanches in vertebrate species which have provided evidence of the critical brain hypothesis. Our intracellular data extend the universality of the hypothesis. In particular, it indicates that the pre-movement buildup activity before the voluntary behavioral onset"from crayfish to human", readiness potential, is shaped by the avalanches. The nervous systems can exploit the universal dynamics for volition across the phylogenetic tree.
Improving Accuracy and Explainability of Online Handwriting Recognition

Handwriting recognition technology allows recognizing a written text from a given data. The recognition task can target letters, symbols, or words, and the input data can be a digital image or recorded by various sensors. A wide range of applications from signature verification to electronic document processing can be realized by implementing efficient and accurate handwriting recognition algorithms. Over the years, there has been an increasing interest in experimenting with different types of technology to collect handwriting data, create datasets, and develop algorithms to recognize characters and symbols. More recently, the OnHW-chars dataset has been published that contains multivariate time series data of the English alphabet collected using a ballpoint pen fitted with sensors. The authors of OnHW-chars also provided some baseline results through their machine learning (ML) and deep learning (DL) classifiers. In this paper, we develop handwriting recognition models on the OnHW-chars dataset and improve the accuracy of previous models. More specifically, our ML models provide $11.3\%$-$23.56\%$ improvements over the previous ML models, and our optimized DL models with ensemble learning provide $3.08\%$-$7.01\%$ improvements over the previous DL models. In addition to our accuracy improvements over the spectrum, we aim to provide some level of explainability for our models to provide more logic behind chosen methods and why the models make sense for the data type in the dataset. Our results are verifiable and reproducible via the provided public repository.
Human Performance Modeling and Rendering via Neural Animated Mesh

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.
Solar-wind electron precipitation on weakly magnetized bodies: The planet Mercury

Mercury is the archetype of a weakly magnetized, airless, telluric body immersed in the solar wind. Due to the lack of any substantial atmosphere, the solar wind directly precipitates on Mercury's surface. Using a 3D fully-kinetic self-consistent plasma model, we show for the first time that solar-wind electron precipitation drives (i) efficient ionization of multiple species (H, He, O and Mn) in Mercury's neutral exosphere and (ii) emission of X-rays from the planet's surface. This is the first, independent evidence of X-ray auroras on Mercury using a numerical approach.
Breaking the speed limit for perfect quantum state transfer

We describe a protocol for perfectly transferring a quantum state from one party to another under the dynamics of a fixed, engineered Hamiltonian. Our protocol combines the concepts of fractional revival, dual rail encoding, and a rare glimpse of the anti-Zeno effect. Remarkably, the transfer happens faster than the speed limit for perfect quantum state transfer [1, 2].
Deep LDA-Pruned Nets for Efﬁcient Facial Gender Classiﬁcation

Many real-time tasks, such as human-computer interac-tion, require fast and efﬁcient facial gender classiﬁcation. Although deep CNN nets have been very effective for a mul-titude of classiﬁcation tasks, their high space and time de-mands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efﬁciency while maintaining high accuracy. Our net is pruned from the VGG-16 model [35] starting from the last convolutional (conv) layer where we ﬁnd neuron activations are highly uncorrelated given the gender. Through Fisher’s Linear Discriminant Analysis (LDA) [8], we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classiﬁcation, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5 3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method [12] (as well as two smaller nets [20, 24]) in terms of accuracy loss and convolutional layers pruning rate are also provided.
tensor categories from quantum groups

In a recent paper [Ki], Kirillov, Jr., defined a *-operation on the morphisms of the representation category of a quantum group Uqg, with lql = 1, using constructions going back to Andersen, Kashiwara and Lusztig. He then conjectured that for any two morphisms f,g: V -* W, the form (f, g) = Trq(g*f) is positive semidefinite for certain roots of unity q, where Trq is the categorical q-trace. As a consequence, the morphisms in a certain quotient category, usually referred to as fusion category, become Hilbert spaces, and one obtains C* categories. We prove this conjecture in this paper for all Lie types. In particular, one obtains for any object in the fusion category a subfactor whose index is the square of its categorical dimension. If the object is simple, the corresponding subfactor is irreducible. We consider the tensor category T of tilting modules of a quantum group Uqg, where g is a simple Lie algebra and q = eri/Id , with d c {1, 2, 3} being the ratio of the square lengths of a long and a short root and 1 being larger than the dual Coxeter number of g. Here, and in the following, we use the notations of Lusztig's book [Lu]. It is shown in [A], [AP] that T contains a semisimple quotient category F whose simple objects are labelled by the dominant weights A which satisfy 0 (A + p) 0 for any morphism f in F. Kirillov defined a *-operation which is functorial; the remaining problem is to prove positivity. The approach in this paper can be sketched in the following way: Assume that we have bases of W1 and W2 with respect to which the generators of the quantum group act via matrices whose coefficients are polynomials in q. Moreover, assume that we can find an idempotent p whose coefficients depend continuously on q such that p(q) commutes with Uqg for q in a neighborhood of 1 containing qo = eri/dl and such that p(qo) projects onto CW1? ,W2, with kernel C' oW *. Moreover, assume we have defined a Hermitian form on W = WI X4 W2 also depending continuously on q which is nondegenerate on p(q)W for all q near 1, and such that it is positive definite on p(1)W. Then it follows by continuity that it has to be positive definite
Mixed Layer Mesoscales for OGCMs: Model development and assessment with T/P, WOCE and Drifter data

We present a model for mixed layer (ML) mesoscale (M) fluxes of an arbitrary tracer in terms of the resolved fields (mean tracer and mean velocity). The treatment of an arbitrary tracer, rather than only buoyancy, is necessary since OGCMs time step T, S, CO2, etc and not buoyancy. The particular case of buoyancy is used to assess the model results. The paper contains three parts: derivation of the results, discussion of the results and assessment of the latter using, among others, WOCE, T/P and Drifter data. Derivation. To construct the M fluxes, we first solve the ML M dynamic equations for the velocity and tracer M fields. The goal of the derivation is to emphasize the different treatments of the non-linear terms in the adiabatic vs. diabatic ocean (deep ocean vs. mixed layer). Results. We derive analytic expressions for the following variables: a) vertical and horizontal M fluxes of an arbitrary tracer, b) M diffusivity in terms of the EKE, c) surface value of the EKE in terms of the vertical M buoyancy flux together with a model for the z-profile of the M EKE, d) tapering function T(z) in terms of the large scale variables; vanishes at the surface and tends to unity below the ML where the stream function smoothly connects with the deep ocean GM form, e) new eddy induced velocity. Assessment. a) the vertical flux naturally vanishes at the ocean surface, as physically required, b) the second z-derivative of the buoyancy flux is negative, implying re-stratification, in agreement with eddy resolving simulations, c) the predicted surface EKE compares well with the T/P-Jason-1 altimetry data in both intensity and geographical distribution, d) the predicted z-profile of the EKE compares well with WOCE data, e) the model predicts both the z-profile and the surface values of the M diffusivity, f) the latter is in accord with the Global Drifter and T/P data.
Magnetism and infrared divergence in a Hubbard-phonon interacting system

We show that a finite Hubbard-phonon interacting system has ferromagnetic or unique spin-singlet ground state under the infrared singular condition. The key tool is a unitary transformation introduced by Arai and Hirokawa. We construct a concrete infrared singular representation using the operator algebraic method. The method is essentially same as one for the van Hove model using the Wightman functional method.
14 N ( 3 He , d ) 15 O as a probe of direct capture in the 14 N ( p , γ ) 15 O reaction

Spectroscopic factors and asymptotic normalization coefficients (ANCs) have been determined for bound states in ${}^{15}\mathrm{O}$ using the ${}^{14}\mathrm{N}{(}^{3}\mathrm{He}{,d)}^{15}\mathrm{O}$ reaction. These results are used to calculate the astrophysical S factor for direct capture in the ${}^{14}\mathrm{N}(p,\ensuremath{\gamma}{)}^{15}\mathrm{O}$ reaction. We also discuss how uncertainties in optical-model parameters influence both the spectroscopic factors and the ANCs, and the effect that this has on the predicted direct-capture reaction rate.
A stable Galerkin reduced order model for coupled fluid–structure interaction problems

A stable reduced order model (ROM) of a linear fluid–structure interaction (FSI) problem involving linearized compressible inviscid flow over a flat linear von Kármán plate is developed. Separate stable ROMs for each of the fluid and the structure equations are derived. Both ROMs are built using the ‘continuous’ Galerkin projection approach, in which the continuous governing equations are projected onto the reduced basis modes in a continuous inner product. The mode shapes for the structure ROM are the eigenmodes of the governing (linear) plate equation. The fluid ROM basis is constructed via the proper orthogonal decomposition. For the linearized compressible Euler fluid equations, a symmetry transformation is required to obtain a stable formulation of the Galerkin projection step in the model reduction procedure. Stability of the Galerkin projection of the structure model in the standard L2 inner product is shown. The fluid and structure ROMs are coupled through solid wall boundary conditions at the interface (plate) boundary. An a priori energy linear stability analysis of the coupled fluid/structure system is performed. It is shown that, under some physical assumptions about the flow field, the FSI ROM is linearly stable a priori if a stabilization term is added to the fluid pressure loading on the plate. The stability of the coupled ROM is studied in the context of a test problem of inviscid, supersonic flow past a thin, square, elastic rectangular panel that will undergo flutter once the non‐dimensional pressure parameter exceeds a certain threshold. This a posteriori stability analysis reveals that the FSI ROM can be numerically stable even without the addition of the aforementioned stabilization term. Moreover, the ROM constructed for this problem properly predicts the maintenance of stability below the flutter boundary and gives a reasonable prediction for the instability growth rate above the flutter boundary. Copyright © 2013 John Wiley & Sons, Ltd.
Avoiding Bias in the Search for Implicit Bias

To revitalize the study of unconscious bias, Gawronski, Ledgerwood, and Eastwick (this issue) propose a paradigm shift away from implicit measures of intergroup attitudes and beliefs. Specifically, researchers should capture discriminatory biases and demonstrate that participants are unaware of the influence of social category cues on their judgments and actions. Individual differences in scores on implicit measures will be useful to predict and better understand implicitly prejudiced behaviors, but the latter should be the collective focus of researchers interested in unconscious biases against social groups. We welcome Gawronski et al.’s (this issue) proposal and seek to build on their insights. We begin by summarizing recent empirical challenges to the implicit measurement approach, which has for the last quarter century focused heavily on capturing individual differences and examining their potential antecedents and consequences. In our view, Gawronski et al. (this issue) underestimate the problems the subfield of implicit bias research is currently facing; the need for a paradigm shift in focus and approach is truly urgent. Although we strongly agree with their basic thesis, we also stress the importance of avoiding various forms of potential bias in the search for implicit bias. First, research in this area should leverage open science innovations such as pre-registration of competing predictions to allow for intellectually and ideologically dissonant conclusions of equal treatment and “reverse” discrimination against members of historically privileged groups. Second, in assessing awareness of bias, researchers should avoid equating unconsciousness with the null hypothesis that evidence of awareness will not emerge, and instead seek positive evidence that the behavioral bias is implicit in nature. Finally, to avoid underestimating the pervasiveness of intergroup bias, scientists should continue to develop and attempt to validate implicit measures of attitudes and beliefs, which may tap latent prejudices expressed in only a small subset of overt actions.
Implicit Bias ≠ Bias on Implicit Measures

Abstract People can behave in a biased manner without being aware that their behavior is biased, an idea commonly referred to as implicit bias. Research on implicit bias has been heavily influenced by implicit measures, in that implicit bias is often equated with bias on implicit measures. Drawing on a definition of implicit bias as an unconscious effect of social category cues on behavioral responses, the current article argues that the widespread equation of implicit bias and bias on implicit measures is problematic on conceptual and empirical grounds. A clear separation of the two constructs will: (1) resolve ambiguities arising from the multiple meanings implied by current terminological conventions; (2) stimulate new research by uncovering important questions that have been largely ignored; (3) provide a better foundation for theories of implicit bias through greater conceptual precision; and (4) highlight the broader significance of implicit bias in a manner that is not directly evident from bias on implicit measures.
Investigating factors related to ethical expectations and motivations among Chinese engineering students

ABSTRACT Research in engineering ethics has examined the effects of education on the ethical knowledge and reasoning of students from mostly WEIRD (Western educated industrialized rich democratic) cultures. However, it is unclear that findings from WEIRD samples are transferable across cultures. China now graduates and employs more STEM (science technology engineering mathematics) majors than any other country, although little work has examined the ethical perspectives and education of these students. Therefore, a study was conducted exploring the kinds of ethical issues Chinese engineering students expect to encounter (expectations), the importance they attach to being ethical (motivations), and their relations to various curricular and extra-curricular factors, including sources of ethical influence, nature and extent of ethics education, and perceived usefulness of ethics education. 163 Chinese engineering majors from two Chinese-foreign educational institutes in Shanghai, China completed a survey. Results indicate participants were most likely to expect to face ethical issues related to fairness, and that the perceived usefulness of ethics education was predictive of both ethical expectations and motivations, followed by encountering instructors who cared about ethics. The extent of ethics education was related to ethical expectations but not motivations. The implications of these findings and directions for future work are discussed.
Existence of Whittaker models related to four dimensional symplectic Galois representations

We show that an irreducible cuspidal automorphic representation of the group GSp(4,A), which is not CAP and whose infinite component belongs to the discrete series, is weakly equivalent to an irreducible generic automorphic cuspidal representation, whose infinite component belongs to the same archimedean local L-packet.
Hybrid Quantum Deep Learning and Variational Quantum Classifier-Based Model for Botnet DGA Attack Detection

: We explored the potentials of the quantum machine learning (ML) approach for cybersecurity domain generation algorithm (DGA) detection applications employing two quantum ML approaches: hybrid quantum-classical deep learning (DL) and variational quantum classifier (VQC) models. We implemented quantum noise models and benchmarked the hybrid quantum-classical DL with the VQC-based one. Our datasets include statistical analysis (entropy, relative entropy, information radius, and reputation score) of 1,000,000 Alexa and 803,333 DGA domain names. Qiskit and Pennylane were utilized for the quantum simulations in our experiments. We found that our proposed hybrid quantum DL outperforms the VQC-based model (94.9% maximum accuracy). Investigating the combinations of 12 types of optimizer algorithms, four feature maps, and four variational form circuits reveal that feature maps and variational forms are critical for the VQC algorithm. We show the usefulness of a quantum circuit architecture consisting of Angle Embedding and Random Layers for a hybrid quantum DL model.
Generalized disks of contractivity for explicit and implicit Runge-Kutta methods

The A -contractivity of Runge-Kutta methods with respect to an inner-product norm, was investigated thoroughly by Butcher and Burrage (who used the term B -stability). Their theory is here extended to contractivity in a circular region tangential to the imaginary axis at the origin. The largest possible circle is calculated for many known explicit Runge-Kutta methods. As a rule it is considerably smaller than the stability region, and in several cases it degenerates to a point.
Specification Tests Indexed by Bandwidths

In this note we consider several goodness-of-fit tests for model specification in non- parametric regression models which are based on kernel methods. In order to circumvent the problem of choosing a bandwidth for the corresponding test statistic we propose to consider the statistics as stochastic processes indexed with bandwidths proportional to the asymptotically optimal bandwidth for the estimation of the regression function. We prove weak convergence of these processes to centered Gaussian processes and suggest to use functionals of these processes as test statistics for the problem of model specification. A bootstrap test is proposed to obtain a good approximation of the nominal level. The results are illustrated by means of a simulation study and the new test is compared with some of the currently available procedures.
Sample size determination for estimation of sensor detection probabilities based on a test variable

Abstract : In this thesis, we study procedures and required sample sizes for estimating the probability of detection as a function of range to target for sensor systems as evaluated by the U.S. Army Yuma Proving Ground. First, we examine the problem within the context of a binomial experiment in order to improve the current estimation method used by the U.S. Army Yuma Proving Ground. Specifically, we evaluate the coverage probabilities and lengths of widely used confidence intervals for a binomial proportion and report the required sample sizes for some specified goals. Although the required sample sizes turn out to be impracticably large, we provide the U.S. Army Yuma Proving Ground with a better understanding of the usual confidence intervals and variability inherent in their current estimation scheme. Second, we show that confidence intervals for a probability of detection as a function of range based on the fit of a simple linear logistic regression model perform much better than the usual confidence intervals for a binomial proportion. Using an empirical approach based on a controlled set of simulations, we then determine the required sample size within the experimental region of interest.