Kernel-Based Tests for Likelihood-Free Hypothesis Testing

Given $n$ observations from two balanced classes, consider the task of labeling an additional $m$ inputs that are known to all belong to \emph{one} of the two classes. Special cases of this problem are well-known: with complete knowledge of class distributions ($n=\infty$) the problem is solved optimally by the likelihood-ratio test; when $m=1$ it corresponds to binary classification; and when $m\approx n$ it is equivalent to two-sample testing. The intermediate settings occur in the field of likelihood-free inference, where labeled samples are obtained by running forward simulations and the unlabeled sample is collected experimentally. In recent work it was discovered that there is a fundamental trade-off between $m$ and $n$: increasing the data sample $m$ reduces the amount $n$ of training/simulation data needed. In this work we (a) introduce a generalization where unlabeled samples come from a mixture of the two classes -- a case often encountered in practice; (b) study the minimax sample complexity for non-parametric classes of densities under \textit{maximum mean discrepancy} (MMD) separation; and (c) investigate the empirical performance of kernels parameterized by neural networks on two tasks: detection of the Higgs boson and detection of planted DDPM generated images amidst CIFAR-10 images. For both problems we confirm the existence of the theoretically predicted asymmetric $m$ vs $n$ trade-off.Comment: 36 pages, 6 figure

Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInP2_2S6

2D layered ferroelectric materials such as CuInP$_2$S6 (CIPS) are promising candidates for novel and high-performance photocatalysts, owning to their ultrathin layer thickness, strong interlayer coupling, and intrinsic spontaneous polarization, while how to control the photocatalytic activity in layered CIPS remains unexplored. In this work, we report for the first time the photocatalytic activity of ferroelectric CIPS for the chemical deposition of silver nanostructures (AgNSs). The results show that the shape and spatial distribution of AgNSs on CIPS are tunable by controlling layer thickness, environmental temperature, and light wavelength. The ferroelectric polarization in CIPS plays a critical role in tunable AgNS photodeposition, as evidenced by layer thickness and temperature dependence experiments. We further reveal that AgNS photodeposition process starts from the active site creation, selective nanoparticle nucleation/aggregation, to the continuous film formation. Moreover, AgNS/CIPS heterostructures prepared by photodeposition exhibit excellent resistance switching behavior and good surface enhancement Raman Scattering activity. Our findings provide new insight into the photocatalytic activity of layered ferroelectrics and offer a new material platform for advanced functional device applications in smart memristors and enhanced chemical sensors.Comment: 18 pages, 5 figure

A comparative analysis of aerosol microphysical, optical and radiative properties during the Spring Festival holiday over Beijing and surrounding regions

Using ground-based data, meteorological observations, and atmospheric environmental monitoring data, a comparative analysis of the microphysical and optical properties, and radiative forcing of aerosols was conducted between three stations in different developed environments during a severe air pollution episode during the Spring Festival over Beijing. During the most polluted period, the daily peak values of the aerosol optical depth were ~1.62, ~1.73, and ~0.74, which were about 2.6, 2.9, and 2.1 times higher than the background levels at the CAMS, Xianghe, and Shangdianzi sites, respectively. The daily peak values of the single scattering albedo were ~0.95, ~0.96, and ~0.87. The volume of fine-mode particles varied from 0.04 to 0.21 µm3 µm-2, 0.06 to 0.17 µm3 µm-2, and 0.01 to 0.10 µm3 µm-2, which were about 0.3 to 5.8, 1.1 to 4.7, and 1.2 to 8.9 times greater than the background values, respectively. The daily absorption aerosol optical depth was ~0.01 to ~0.13 at CAMS, ~0.03 to ~0.14 at Xianghe, and ~0.01 to ~0.09 at Shangdianzi, and the absorption Ångström exponents reflected a significant increase in organic aerosols over CAMS and Xianghe and in black carbon over Shangdianzi. Aerosol radiative forcing at the bottom of the atmosphere varied from -20 to -130, -40 to -150, and -10 to -110 W m-2 for the whole holiday period, indicating the cooling effect. The potential source contribution function and concentration-weighted trajectory analysis showed that Beijing, the southern parts of Hebei and Shanxi, and the central northern part of Shandong contributed greatly to the pollution

Spin pinning effect to reconstructed oxyhydroxide layer on ferromagnetic oxides for enhanced water oxidation.

Producing hydrogen by water electrolysis suffers from the kinetic barriers in the oxygen evolution reaction (OER) that limits the overall efficiency. With spin-dependent kinetics in OER, to manipulate the spin ordering of ferromagnetic OER catalysts (e.g., by magnetization) can reduce the kinetic barrier. However, most active OER catalysts are not ferromagnetic, which makes the spin manipulation challenging. In this work, we report a strategy with spin pinning effect to make the spins in paramagnetic oxyhydroxides more aligned for higher intrinsic OER activity. The spin pinning effect is established in oxideFM/oxyhydroxide interface which is realized by a controlled surface reconstruction of ferromagnetic oxides. Under spin pinning, simple magnetization further increases the spin alignment and thus the OER activity, which validates the spin effect in rate-limiting OER step. The spin polarization in OER highly relies on oxyl radicals (O∙) created by 1st dehydrogenation to reduce the barrier for subsequent O-O coupling

Multi-Object Grasping -- Stochastic Grasping from a Pile

Grasping multiple objects at once from a pile is common for humans. It makes us efficient in pick and transfer tasks. It is essential for a robot to gain multi-object grasping capability (MOG). This paper defines the multi-object grasping problem and introduces several novel multi-object grasping techniques. These techniques include probability-based pre-grasp potential calculation, a stochastic flexing/extending routine, obtaining end-grasp types, and estimating the number of objects in a grasp. It also proposes a new stochastic grasping strategy for grasping a desired number of objects

Learning Cross-lingual Word Embeddings via Matrix Co-factorization

A joint-space model for cross-lingual distributed representations generalizes language-invariant semantic features. In this paper, we present a matrix co-factorization framework for learning cross-lingual word embeddings. We explicitly define monolingual training objectives in the form of matrix de-composition, and induce cross-lingual constraints for simultaneously factorizing monolingual matrices. The cross-lingual constraints can be derived from parallel corpora, with or without word alignments. Empirical results on a task of cross-lingual document classification show that our method is effective to encode cross-lingual knowledge as constraints for cross-lingual word embeddings.

Hybrid Projection Algorithms for Asymptotically Strict Quasi-ϕ-Pseudocontractions

A new nonlinear mapping is introduced. Hybrid projection algorithms are considered for the class of new nonlinear mappings. Strong convergence theorems are established in a real Banach space

Learning Cross-lingual Word Embeddings via Matrix Co-factorization

A joint-space model for cross-lingual distributed representations generalizes language-invariant semantic features. In this paper, we present a matrix co-factorization framework for learning cross-lingual word embeddings. We explicitly define monolingual training objectives in the form of matrix de-composition, and induce cross-lingual constraints for simultaneously factorizing monolingual matrices. The cross-lingual constraints can be derived from parallel corpora, with or without word alignments. Empirical results on a task of cross-lingual document classification show that our method is effective to encode cross-lingual knowledge as constraints for cross-lingual word embeddings.