Miracle-dz/UniFAR-FTUs huggingface dataset:
Wearing Out Your Shoes to Prevent Someone Else from Stepping into Them: Anticipated Regret and Social Takeover in Sequential Decisions

Comparisons with counterfactual outcomes can influence choices in sequential decisions. We examine the effect of anticipated regret, and social takeover - the knowledge that someone else might take one's place - on persistence on an investment task. Some participants received feedback about what would have happened if they had continued investing and others did not. Some knew that another person had the opportunity to pick up their investment where they left off and others did not. Data collected from 84 dyads showed effects of both experimental manipulations. Participants invest longer, on average, when another person could take over from their previous investments, and when feedback was provided. Both anticipated regret and social takeover appear to increase the tendency to stick with an investment.
Procedure Optimization on Extraction and Separation of Red Dates Polysaccharide

Effect of temperature, proportion and time on extraction of the red dates polysaccharide were studied by orthogonalexperiment design and the optimum proportion A2B2C3 was chosen preliminarily. Meanwhile the author analyzed the componentof polysaccharide by infrared spectrum, ultraviolet spectrum and paper chromatography. The purification from the decoloration,removing proteins and others were deeply studied, the results showed that the red dates polysaccharide was a mono-ingredient,composed of D-Galactose and D-Xylose. The ways of active charcoal decoloration and proteins Sevag removing have significantefficiency.
Four Amazing Positivities with Dimers/i-Matchings

We collect a number of striking recent results in a study of dimers on inﬁnite r-regular bipartite lattices and also on r-regular bipartite graphs. We clearly separate rigorously proven results from conjectures. A primary goal is to show people: here is a ﬁeld which is ripe for further interesting research. We separate four classes of endeavor, of which we here extract two items to whet one’s appetite. Primo, the hyper-rectangular lattices of every dimension have their ﬁrst 20 virial coeﬃcients positive. (One has no understanding of this yet! ) Secondo, all regular bipartite graphs with less than 14 vertices satisfy graph positivity, deﬁned below. (Here there is some understanding.)
Compressing Heavy-Tailed Weight Matrices for Non-Vacuous Generalization Bounds

Heavy-tailed distributions have been studied in statistics, random matrix theory, physics, and econometrics as models of correlated systems, among other domains. Further, heavy-tail distributed eigenvalues of the covariance matrix of the weight matrices in neural networks have been shown to empirically correlate with test set accuracy in several works (e.g. arXiv:1901.08276), but a formal relationship between heavy-tail distributed parameters and generalization bounds was yet to be demonstrated. In this work, the compression framework of arXiv:1802.05296 is utilized to show that matrices with heavy-tail distributed matrix elements can be compressed, resulting in networks with sparse weight matrices. Since the parameter count has been reduced to a sum of the non-zero elements of sparse matrices, the compression framework allows us to bound the generalization gap of the resulting compressed network with a non-vacuous generalization bound. Further, the action of these matrices on a vector is discussed, and how they may relate to compression and resilient classification is analyzed.
Cascading Bandit Under Differential Privacy

This paper studies differential privacy (DP) and local differential privacy (LDP) in cascading bandits. Under DP, we propose a UCB-based algorithm which guarantees ϵ-indistinguishability and a regret of $\mathcal{O}\left( {{{\left( {\frac{{\log T}}{ \in }} \right)}^{1 + \xi }}} \right)$ for an arbitrarily small ξ. This result significantly improves $O\left( {\frac{{{{\log }^3}T}}{ \in }} \right)$ in the previous work. Under (ϵ, δ)-LDP, we relax the K2 dependence through the tradeoff between privacy budget ϵ and error probability δ, and obtain a regret of ${\text{ }}\mathcal{O}{\text{ }}\left( {\frac{{K\log (1/\delta )\log T}}{{{ \in ^2}}}} \right)$, where K is the size of the arm subset. This result holds for both Gaussian mechanism and Laplace mechanism by analyses on the composition. Extensive experiments corroborate our theoretic findings.
On the Optimality of the Stationary Solution of Secrecy Rate Maximization for MIMO Wiretap Channel

To achieve perfect secrecy in a multiple-input multiple-output (MIMO) Gaussian wiretap channel (WTC), we need to find its secrecy capacity and optimal signaling, which involves solving a difference of convex functions program known to be non-convex for the non-degraded case. To deal with this, a class of existing solutions have been developed but only local optimality is guaranteed by standard convergence analysis. Interestingly, our extensive numerical experiments have shown that these local optimization methods indeed achieve global optimality. In this letter, we provide an analytical proof for this observation. To achieve this, we show that the Karush-Kuhn-Tucker (KKT) conditions of the secrecy rate maximization problem admit a unique solution for both degraded and non-degraded cases. Motivated by this, we also propose a low-complexity algorithm to find a stationary point. Numerical results are presented to verify the theoretical analysis.
Subaru Telescope —History, active/adaptive optics, instruments, and scientific achievements—

The Subaru Telescopea) is an 8.2 m optical/infrared telescope constructed during 1991–1999 and has been operational since 2000 on the summit area of Maunakea, Hawaii, by the National Astronomical Observatory of Japan (NAOJ). This paper reviews the history, key engineering issues, and selected scientific achievements of the Subaru Telescope. The active optics for a thin primary mirror was the design backbone of the telescope to deliver a high-imaging performance. Adaptive optics with a laser-facility to generate an artificial guide-star improved the telescope vision to its diffraction limit by cancelling any atmospheric turbulence effect in real time. Various observational instruments, especially the wide-field camera, have enabled unique observational studies. Selected scientific topics include studies on cosmic reionization, weak/strong gravitational lensing, cosmological parameters, primordial black holes, the dynamical/chemical evolution/interactions of galaxies, neutron star mergers, supernovae, exoplanets, proto-planetary disks, and outliers of the solar system. The last described are operational statistics, plans and a note concerning the culture-and-science issues in Hawaii.
Delineating chiral separation effect in two-color dense QCD

We study the chiral separation effect (CSE) in two-color and two-flavor QCD (QC2D) to delineate quasiparticle pictures in dense matter from low to high temperatures. Both massless and massive quarks are discussed. We particularly focus on the high density domain where diquarks form a color singlet condensate with the electric charge 1/3. The condensate breaks baryon number and U(1)A axial symmetry, and induces the electromagnetic Meissner effects. Within a quark quasiparticle picture, we compute the chiral separation conductivity at one-loop. We have checked that NambuGoldstone modes, which should appear in the improved vertices as required by the Ward-Takahashi identities, do not contribute to the chiral separation conductivity due to their longitudinal natures. In the static limit, the destructive interferences in the particle-hole channel, as in usual Meissner effects, suppress the conductivity (in chiral limit, to 1/3 of the normal phase’s). This locally breaks the universality of the CSE coefficients, provided quasiparticle pictures are valid in the bulk matter.
On Incremental Structure from Motion Using Lines

Humans tend to build environments with structure, which consists of mainly planar surfaces. From the intersection of planar surfaces arise straight lines. Lines have more degrees of freedom than points. Thus, line-based structure-from-motion (SfM) provides more information about the environment. In this article, we present solutions for SfM using lines, namely, incremental SfM. These approaches consist of designing state observers for a camera’s dynamical visual system looking at a 3-D line. We start by presenting a model that uses spherical coordinates for representing the line’s moment vector. We show that this parameterization has singularities, and, therefore, we introduce a more suitable model that considers the line’s moment and shortest viewing ray. Concerning the observers, we present two different methodologies. The first uses a memory-less state-of-the-art framework for dynamic visual systems. Since the previous states of the robotic agent are accessible—while performing the 3-D mapping of the environment—the second approach aims at exploiting the use of memory to improve the estimation accuracy and convergence speed. The two models and the two observers are evaluated in simulation and real data, where mobile and manipulator robots are used.
A Bayesian Approach for Partial Gaussian Graphical Models With Sparsity

We explore various Bayesian approaches to estimate partial Gaussian graphical models. Our hierarchical structures enable to deal with single-output as well as multiple-output linear regressions, in small or high dimension, enforcing either no sparsity, sparsity, group sparsity or even sparse-group sparsity for a bi-level selection through partial correlations (direct links) between predictors and responses, thanks to spike-and-slab priors corresponding to each setting. Adaptative and global shrinkages are also incorporated in the Bayesian modeling of the direct links. An existing result for model selection consistency is reformulated to stick to our sparse and group-sparse settings, providing a theoretical guarantee under some technical assumptions. Gibbs samplers are developed and a simulation study shows the eﬃciency of our models which give very competitive results, especially in terms of support recovery. To conclude, a real dataset is investigated.
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package

Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of multivariate time series data. They have been widely used in macroeconomics, financial econometrics, neuroscience and functional genomics. In many applications, the data exhibit structural changes in their autoregressive dynamics, which correspond to changes in the transition matrices of the VAR model that specify such dynamics. We present the R package VARDetect that implements two classes of algorithms to detect multiple change points in piecewise stationary VAR models. The first exhibits sublinear computational complexity in the number of time points and is best suited for structured sparse models, while the second exhibits linear time complexity and is designed for models whose transition matrices are assumed to have a low rank plus sparse decomposition. The package also has functions to generate data from the various variants of VAR models discussed, which is useful in simulation studies, as well as to visualize the results through network layouts.
Simultaneous Distributed Estimation and Attack Detection/Isolation in Social Networks: Structural Observability, Kronecker-Product Network, and Chi-Square Detector

This paper considers distributed estimation of linear systems when the state observations are corrupted with Gaussian noise of unbounded support and under possible random adversarial attacks. We consider sensors equipped with single time-scale estimators and local chi-square $(\chi^{2})$ detectors to simultaneously observe the states, share information, fuse the noise/attack-corrupted data locally, and detect possible anomalies in their own observations. While this scheme is applicable to a wide variety of systems associated with full-rank (invertible) matrices, we discuss it within the context of distributed inference in social networks. The proposed technique outperforms existing results in the sense that: (i) we consider Gaussian noise with no simplifying upper-bound assumption on the support; (ii) all existing $\chi^{2}$-based techniques are centralized while our proposed technique is distributed, where the sensors locally detect attacks, with no central coordinator, using specific probabilistic thresholds; and (iii) no local-observability assumption at a sensor is made, which makes our method feasible for large-scale social networks. Moreover, we consider a Linear Matrix Inequalities (LMI) approach to design block-diagonal gain (estimator) matrices under appropriate constraints for isolating the attacks.
EXoN: EXplainable encoder Network

We propose a new semi-supervised learning method of Vari- ational AutoEncoder (VAE) which yields a customized and explainable latent space by EXplainable encoder Network (EXoN). Customization means a manual design of latent space layout for speciﬁc labeled data. To improve the per- formance of our VAE in a classiﬁcation task without the loss of performance as a generative model, we employ a new semi-supervised classiﬁcation method called SCI (Soft-label Consistency Interpolation). The classiﬁcation loss and the Kullback-Leibler divergence play a crucial role in constructing explainable latent space. The variability of gener- ated samples from our proposed model depends on a speciﬁc subspace, called activated latent subspace. Our numerical re- sults with MNIST and CIFAR-10 datasets show that EXoN produces an explainable latent space and reduces the cost of investigating representation patterns on the latent space.
The Role of Frustration in Collective Decision-Making Dynamical Processes on Multiagent Signed Networks

In this article, we consider a collective decision-making process in a network of agents described by a nonlinear interconnected dynamical model with sigmoidal nonlinearities and signed interaction graph. The decisions are encoded in the equilibria of the system. The aim is to investigate this multiagent system when the signed graph representing the community is not structurally balanced and in particular as we vary its frustration, i.e., its distance to structural balance. The model exhibits bifurcations, and a “social effort” parameter, added to the model to represent the strength of the interactions between the agents, plays the role of bifurcation parameter in our analysis. We show that, as the social effort increases, the decision-making dynamics exhibit a pitchfork bifurcation behavior where, from a deadlock situation of “no decision” (i.e., the origin is the only globally stable equilibrium point), two possible (alternative) decision states for the community are achieved (corresponding to two nonzero locally stable equilibria). The value of social effort for which the bifurcation is crossed (and a decision is reached) increases with the frustration of the signed network.
UHLENBECK COMPACTNESS AND OPTIMAL REGULARITY FOR YANG-MILLS THEORY WITH LORENTZIAN GEOMETRY AND NON-COMPACT LIE GROUPS

We extend the authors’ prior theory of the RT-equations from the setting of affine connections, to the general setting of connections defined on vector bundles over arbitrary manifolds, including YangMills connections over Lorentzian manifolds in Physics. By this, our theory of the RT-equations extends optimal regularity and Uhlenbeck compactness from the case of vector bundles over Riemannian manifolds with compact Lie group, to vector bundles over arbitrary manifolds, allowing for both compact and non-compact Lie groups. Our results here apply to L connections, p > n.
Post-Training Sparsity-Aware Quantization

Quantization is a technique used in deep neural networks (DNNs) to increase execution performance and hardware efficiency. Uniform post-training quantization (PTQ) methods are common, since they can be implemented efficiently in hardware and do not require extensive hardware resources or a training set. Mapping FP32 models to INT8 using uniform PTQ yields models with negligible accuracy degradation; however, reducing precision below 8 bits with PTQ is challenging, as accuracy degradation becomes noticeable, due to the increase in quantization noise. In this paper, we propose a sparsity-aware quantization (SPARQ) method, in which the unstructured and dynamic activation sparsity is leveraged in different representation granularities. 4-bit quantization, for example, is employed by dynamically examining the bits of 8-bit values and choosing a window of 4 bits, while first skipping zero-value bits. Moreover, instead of quantizing activation-by-activation to 4 bits, we focus on pairs of 8-bit activations and examine whether one of the two is equal to zero. If one is equal to zero, the second can opportunistically use the other's 4-bit budget; if both do not equal zero, then each is dynamically quantized to 4 bits, as described. SPARQ achieves minor accuracy degradation, 2x speedup over widely used hardware architectures, and a practical hardware implementation. The code is available at this https URL.
Size Distribution Control of Copper Nanoparticle: Effect of Precursor Morphology

In the liquid phase synthesis of metallic nanoparticles by electroless deposition, the morphology of product is strongly dependent on numerous synthesis conditions; for example, the type or quantity of solvent, precursor, reductant, and dispersant species. In this work, the influence of the surface area of precursor on the size distribution of synthesized nanoparticles was investigated using a kinetic evaluation by quartz crystal microbalance (QCM) measurement. We clarified the correlation between the morphology of product and the deposition behavior such as termination point and deposition rate derived by QCM measurement. We also measured the immersion potential of the reaction solution, and found that the nucleation and growth of Cu nanoparticles were supposed to proceed with the reduction of Cu(II) ion to Cu as long as the Cu(II) sufficiently exist in the solution. Therefore, the saturation of deposition amount on QCM indicated the consumption of precursors. By this estimation, we demonstrated the size distribution was predictable from QCM measurement, and it was controllable by regulating the morphology of precursor.
Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks

SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree. The accuracy of these systems is limited since deep learning neural networks such as CNNs (Convolutional Neural Networks) need a significantly large quantity of data for training in order to produce good quality results. Since the disease is relatively recent and more focus has been on CXR (Chest XRay) images, the available chest CT Scan image dataset is much less. We propose a method, by utilizing GANs, to generate synthetic chest CT images of both positive and negative COVID-19 patients. Using a pre-built predictive model, we concluded that around 40% of the generated images are correctly predicted as COVID-19 positive. The dataset thus generated can be used to train a CNN-based classifier which can help determine COVID-19 in a patient with greater accuracy.
Magnetic excitations in infinite-layer nickelates

Looking for magnetic clues Thin films of the neodymium nickelate NdNiO2 doped with strontium have recently been found to be superconducting. This materials class bears structural and electronic similarities to the famed cuprate superconductors, but how far the analogy goes remains unclear. Lu et al. used resonant inelastic x-ray scattering to look for magnetism, which exists in the cuprates, in Nd1-xSrxNiO2 films (see the Perspective by Benckiser). The authors observed magnetic modes in the undoped compound that had a doping evolution consistent with the behavior of a doped Mott insulator. Science, abd7726, this issue p. 213; see also abi6855, p. 157 Resonant inelastic x-ray scattering is used to probe the magnetic excitations in Nd1−xSrxNiO2 films. The discovery of superconductivity in infinite-layer nickelates brings us tantalizingly close to a material class that mirrors the cuprate superconductors. We measured the magnetic excitations in these nickelates using resonant inelastic x-ray scattering at the Ni L3-edge. Undoped NdNiO2 possesses a branch of dispersive excitations with a bandwidth of approximately 200 milli–electron volts, which is reminiscent of the spin wave of strongly coupled, antiferromagnetically aligned spins on a square lattice. The substantial damping of these modes indicates the importance of coupling to rare-earth itinerant electrons. Upon doping, the spectral weight and energy decrease slightly, whereas the modes become overdamped. Our results highlight the role of Mottness in infinite-layer nickelates.
StructuralLM: Structural Pre-training for Form Understanding

Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. Specifically, we pre-train StructuralLM with two new designs to make the most of the interactions of cell and layout information: 1) each cell as a semantic unit; 2) classification of cell positions. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks, including form understanding (from 78.95 to 85.14), document visual question answering (from 72.59 to 83.94) and document image classification (from 94.43 to 96.08).
Robust Bayesian Nonparametric Variable Selection for Linear Regression

Spike-and-slab and horseshoe regression are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real-world statistics and machine learning applications. In this work, we propose a Bayesian nonparametric approach to linear regression that performs variable selection while accounting for outliers and heteroskedasticity. Our proposed model is an instance of a Dirichlet process scale mixture model with the advantage that we can derive the full conditional distributions of all parameters in closed form, hence producing an eﬃcient Gibbs sampler for posterior inference. Moreover, we present how to extend the model to account for heavy-tailed response variables. The performance of the model is tested against competing algorithms on synthetic and real-world datasets.