Sports, Gigs, and TikToks: Multi-Channel Advertising of Oral Nicotine Pouches

Oral nicotine pouches, which contain fewer harmful constituents than traditional tobacco products, are being increasingly marketed and sold. In this paper, we use images we collected in Great Britain between 2021 and 2023, along with a social media scan of Instagram and TikTok in 2023 to analyse the marketing strategies of these pouches on three key marketing channels — online media, sports sponsorships, and out-of-home advertising. Findings reveal the extensive use of visually appealing content, influencer partnerships, and event sponsorships that are potentially targeting young and naive audiences. Despite this, survey data indicates that nicotine pouch use in Great Britain remains minimal. However, a notable shift in public health impact could arise if these marketing efforts start to bear more fruit. The study underscores the need for balanced policy measures that increase access to reduced harm alternatives for existing tobacco and nicotine users while minimising exposure to youth and non-users. Continuous monitoring and regulatory adjustments are essential to address the evolving landscape of nicotine pouch marketing

Counting Objects in a Robotic Hand

A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping. The count plays an important role in determining the robot's next move and the outcome and efficiency of the whole pick-place process. This paper presents a data-driven contrastive learning-based counting classifier with a modified loss function as a simple and effective approach for object counting despite significant occlusion challenges caused by robotic fingers and objects. The model was validated against other models with three different common shapes (spheres, cylinders, and cubes) in simulation and in a real setup. The proposed contrastive learning-based counting approach achieved above 96\% accuracy for all three objects in the real setup

Towards Effective Measures for Curbing the Illegal Wildlife Trade: A Comprehensive Approach with ARIMA Modeling and Responsible Party Evaluation

Illegal wildlife trade has become an urgent global problem that threatens global ecosystems, biodiversity and sustainable development. This problem requires us to propose comprehensive measures to significantly curb illegal wildlife trade.We used data from government work reports from 2014 to 2023 to establish an evaluation system for the responsible parties, and found that wildlife conservation is very important to the Chinese government using the AHP-Entropy weight method. We developed relevant measures to reduce illegal wildlife trade based on Citespace's literature research method. Then we collected public data from Chinese governmental departments from 2010 to 2023, and used ARIMA model to predict the future of the initiatives we developed to reduce illegal wildlife trade. By collecting public data from the China Wildlife Wildlife Enforcement Agency and fitting the state of wildlife conservation based on China's national conditions over the past 15 years, we evaluated the available resources and found that while the overall trend of the Chinese government can reduce illegal trade practices, the trend is not stable in the short term. China's wildlife protection agencies can continue to increase enforcement of this problem to better reduce the occurrence of illegal wildlife trade behavior

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

Density estimation using the perceptron

We propose a new density estimation algorithm. Given $n$ i.i.d. samples from a distribution belonging to a class of densities on $\mathbb{R}^d$, our estimator outputs any density in the class whose ''perceptron discrepancy'' with the empirical distribution is at most $O(\sqrt{d/n})$. The perceptron discrepancy between two distributions is defined as the largest difference in mass that they place on any halfspace of $\mathbb{R}^d$. It is shown that this estimator achieves expected total variation distance to the truth that is almost minimax optimal over the class of densities with bounded Sobolev norm and Gaussian mixtures. This suggests that regularity of the prior distribution could be an explanation for the efficiency of the ubiquitous step in machine learning that replaces optimization over large function spaces with simpler parametric classes (e.g. in the discriminators of GANs). We generalize the above to show that replacing the ''perceptron discrepancy'' with the generalized energy distance of Sz\'ekeley-Rizzo further improves total variation loss. The generalized energy distance between empirical distributions is easily computable and differentiable, thus making it especially useful for fitting generative models. To the best of our knowledge, it is the first example of a distance with such properties for which there are minimax statistical guarantees.Comment: 47 page

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

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing. The source code is available at https://github.com/BEAM-Labs/ContraNovo.Comment: This paper has been accepted by AAAI 202

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