Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitive and selective detection of alcohols via fluorescence modulation

Abstract Reported herein is the selective detection of aliphatic alcohols using cyclodextrin-promoted, proximity-induced fluorescence modulation of a high-quantum yield fluorophore. This fluorescence modulation occurred when the analyte was held in close proximity to the fluorophore via non-covalent cyclodextrin–analyte–fluorophore interactions, and led to unique modulation responses for each analyte, fluorophore and cyclodextrin investigated. These changes in fluorescence were used for the generation of an array using linear discriminant analysis that successfully generated unique pattern identifiers for 99% of the investigated analytes, and could detect alcohols at micromolar concentrations. These results represent a fundamentally new detection approach for these challenging analytes, and have significant potential in the development of novel detection schemes.
Current Challenges and Future Directions in Podcast Information Access

Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driven by word of mouth. In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.
The Nature of Jets in Double-peaked Emission-line AGN in the KISSR Sample

Double-peaked emission-line AGN (DPAGN) have been regarded as binary black hole candidates. We present here results from parsec-scale radio observations with the Very Long Baseline Array (VLBA) of five DPAGN belonging to the KISSR sample of emission-line galaxies. This work concludes our pilot study of nine type 2 Seyfert and LINER DPAGN from the KISSR sample. In the nine sources, dual compact cores are only detected in the “offset AGN,” KISSR 102. However, the overall incidence of jets in the eight sources that were detected with the VLBA is ≥60%. We find a difference in the “missing flux density” going from the Very Large Array to VLBA scales between Seyferts and LINERs, with LINERs showing less missing flux density on parsec-scales. Using the emission-line modeling code, MAPPINGS III, we find that the emission lines are likely to be influenced by jets in 5/9 sources. Jet-medium interaction is the likely cause of the emission-line splitting observed in the SDSS spectra of these sources. Jets in radio-quiet AGN are therefore energetically capable of influencing their parsec- and kpc-scale environments, which makes them agents of “radio AGN feedback” (similar to radio-loud AGN).
Layer Folding: Neural Network Depth Reduction using Activation Linearization

Despite the increasing prevalence of deep neural networks, their applicability in resource-constrained devices is limited due to their computational load. While modern devices exhibit a high level of parallelism, real-time latency is still highly dependent on networks' depth. Although recent works show that below a certain depth, the width of shallower networks must grow exponentially, we presume that neural networks typically exceed this minimal depth to accelerate convergence and incrementally increase accuracy. This motivates us to transform pre-trained deep networks that already exploit such advantages into shallower forms. We propose a method that learns whether non-linear activations can be removed, allowing to fold consecutive linear layers into one. We apply our method to networks pre-trained on CIFAR-10 and CIFAR-100 and find that they can all be transformed into shallower forms that share a similar depth. Finally, we use our method to provide more efficient alternatives to MobileNetV2 and EfficientNet-Lite architectures on the ImageNet classification task.
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains challenging. In this paper, we present the first unified framework CROP (Certifying Robust Policies for RL) to provide robustness certification on both action and reward levels. In particular, we propose two robustness certification criteria: robustness of per-state actions and lower bound of cumulative rewards. We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification. Empirically, we apply CROP to evaluate several existing empirically robust RL algorithms, including adversarial training and different robust regularization, in four environments (two representative Atari games, Highway, and CartPole). Furthermore, by evaluating these algorithms against adversarial attacks, we demonstrate that our certifications are often tight. All experiment results are available at website https://crop-leaderboard.github. io.
Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall

The R package optimall offers a collection of functions that efficiently streamline the design process of sampling in surveys ranging from simple to complex. The package's main functions allow users to interactively define and adjust strata cut points based on values or quantiles of auxiliary covariates, adaptively calculate the optimum number of samples to allocate to each stratum using Neyman or Wright allocation, and select specific IDs to sample based on a stratified sampling design. Using real-life epidemiological study examples, we demonstrate how optimall facilitates an efficient workflow for the design and implementation of surveys in R. Although tailored towards multi-wave sampling under two- or three-phase designs, the R package optimall may be useful for any sampling survey.
Explainable AI for Natural Adversarial Images

Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends on humans understanding the AI well enough to predict when it is likely to make a mistake. In previous work we have found that humans tend to assume that the AI's decision process mirrors their own. Here we evaluate if methods from explainable AI can disrupt this assumption to help participants predict AI classifications for adversarial and standard images. We find that both saliency maps and examples facilitate catching AI errors, but their effects are not additive, and saliency maps are more effective than examples.
Clustering inference in multiple groups

Inference in clustering is paramount to uncovering inherent group structure in data. Clustering methods which assess statistical significance have recently drawn attention owing to their importance for the identification of patterns in high dimensional data with applications in many scientific fields. We present here a U-statistics based approach, specially tailored for high-dimensional data, that clusters the data into three groups while assessing the significance of such partitions. Because our approach stands on the U-statistics based clustering framework of the methods in R package uclust, it inherits its characteristics being a non-parametric method relying on very few assumptions about the data, and thus can be applied to a wide range of dataset. Furthermore our method aims to be a more powerful tool to find the best partitions of the data into three groups when that particular structure is present. In order to do so, we first propose an extension of the test U-statistic and develop its asymptotic theory. Additionally we propose a ternary non-nested significance clustering method. Our approach is tested through multiple simulations and found to have more statistical power than competing alternatives in all scenarios considered. Applications to peripheral blood mononuclear cells and to image recognition shows the versatility of our proposal, presenting a superior performance when compared with other approaches.
Prevalence and Propagation of Fake News

ABSTRACT In recent years, scholars have raised concerns on the effects that unreliable news, or “fake news,” has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader’s own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political belief and read articles of opposing political bias. Supplementary materials for this article are available online.
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes

We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choice of prior, of conditional MAC-Bayesian (Mean Approximately Correct) bounds, itself derived from conditional PAC-Bayesian bounds, where `conditional' means that one can use priors conditioned on a joint training and ghost sample. This allows us to get nontrivial PAC-Bayes and MI-style bounds for general VC classes, something recently shown to be impossible with standard PAC-Bayesian/MI bounds. Second, it allows us to get faster rates of order $O \left(({\text{KL}}/n)^{\gamma}\right)$ for $\gamma>1/2$ if a Bernstein condition holds and for exp-concave losses (with $\gamma=1$), which is impossible with both standard PAC-Bayes generalization and MI bounds. Our work extends the recent work by Steinke and Zakynthinou [2020] who handle MI with VC but neither PAC-Bayes nor fast rates, the recent work of Hellstr\"om and Durisi [2020] who extend the latter to the PAC-Bayes setting via a unifying exponential inequality, and Mhammedi et al. [2019] who initiated fast rate PAC-Bayes generalization error bounds but handle neither MI nor general VC classes.
Comagnetometer probes of dark matter and new physics

Modern comagnetometry is—in absolute energy units—the most sensitive experimental technique for measuring the energy splitting between quantum states, with certain implementations measuring the nuclear spin-up/spin-down splitting at the 10−26 eV level. By measuring and subtracting the leading magnetic effects on the spins, comagnetometry can be used to study non-standard-model spin interactions. New physics scenarios that comagnetometers can probe include EDMs, violations of Lorentz invariance, Goldstone bosons from new high-energy symmetries, spin-dependent and CP-violating long-range forces, and axionic dark matter. We describe the many implementations that have been developed and optimized for these applications, and consider the prospects for improvements in the technique. Based purely on existing technology, there is room for several orders of magnitude in further improvement in statistical sensitivity. We also evaluate sources of systematic error and instability that may limit attainable improvements.
The 2021 Image Similarity Dataset and Challenge

This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of image transformations such as automated transformations, hand-crafted image edits and machine-learning based manipulations. This mimics real-life cases appearing in social media, for example for integrity-related problems dealing with misinformation and objectionable content. The strength of the image manipulations, and therefore the difficulty of the benchmark, is calibrated according to the performance of a set of baseline approaches. Both the query and reference set contain a majority of"distractor"images that do not match, which corresponds to a real-life needle-in-haystack setting, and the evaluation metric reflects that. We expect the DISC21 benchmark to promote image copy detection as an important and challenging computer vision task and refresh the state of the art. Code and data are available at https://github.com/facebookresearch/isc2021
ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks

Deep neural networks have enormous representational power which leads them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and enhance their generalization capabilities. Recently, channel shuffle operation has been introduced for mixing channels in group convolutions in resource efficient networks in order to reduce memory and computations. This paper studies the operation of channel shuffle as a regularization technique in deep convolutional networks. We show that while random shuffling of channels during training drastically reduce their performance, however, randomly shuffling small patches between channels significantly improves their performance. The patches to be shuffled are picked from the same spatial locations in the feature maps such that a patch, when transferred from one channel to another, acts as structured noise for the later channel. We call this method"ShuffleBlock". The proposed ShuffleBlock module is easy to implement and improves the performance of several baseline networks on the task of image classification on CIFAR and ImageNet datasets. It also achieves comparable and in many cases better performance than many other regularization methods. We provide several ablation studies on selecting various hyperparameters of the ShuffleBlock module and propose a new scheduling method that further enhances its performance.
Optimum-statistical collaboration towards efficient black-box optimization

With increasingly more hyperparameters involved in their training, machine learning systems demand a better understanding of hyperparameter tuning automation. This has raised interest in studies of provably black-box optimization, which is made more practical by better exploration mechanism implemented in algorithm design, managing the ﬂux of both optimization and statistical errors. Prior efforts focus on delineating optimization errors, but this is deﬁcient: black-box optimization algorithms can be inefﬁcient without considering heterogeneity among reward samples. In this paper, we make the key delineation on the role of statistical uncertainty in black-box optimization, guiding a more efﬁcient algorithm design. We introduce optimum-statistical collaboration , a framework of managing the interaction between optimization error ﬂux and statistical error ﬂux evolving in the optimization process. Inspired by this framework, we propose the VHCT algorithms for objective functions with only local-smoothness assumptions. In theory, we prove our algorithm enjoys rate-optimal regret bounds; in experiments, we show the algorithm outperforms prior efforts in extensive settings.
A structure-preserving surrogate model for the closure of the moment system of the Boltzmann equation using convex deep neural networks

Direct simulation of physical processes on a kinetic level is prohibitively expensive in aerospace applications due to the extremely high dimension of the solution spaces. In this paper, we consider the moment system of the Boltzmann equation, which projects the kinetic physics onto the hydrodynamic scale. The unclosed moment system can be solved in conjunction with the entropy closure strategy. Using an entropy closure provides structural benefits to the physical system of partial differential equations. Usually computing such closure of the system spends the majority of the total computational cost, since one needs to solve an ill-conditioned constrained optimization problem. Therefore, we build a neural network surrogate model to close the moment system, which preserves the structural properties of the system by design, but reduces the computational cost significantly. Numerical experiments are conducted to illustrate the performance of the current method in comparison to the traditional closure.
ALMA Lensing Cluster Survey: A spectral stacking analysis of [C II] in lensed z ∼ 6 galaxies

Context. The properties of galaxies at redshift z > 6 hold the key to our understanding of the early stages of galaxy evolution and can potentially identify the sources of the ultraviolet radiation that give rise to the epoch of reionisation. The far-infrared cooling line of [C II] at 158 μm is known to be bright and correlate with the star formation rate (SFR) of low-redshift galaxies, and hence is also suggested to be an important tracer of star formation and interstellar medium properties for very high-redshift galaxies. Aims. With the aim to study the interstellar medium properties of gravitationally lensed galaxies at z > 6, we search for [C II] and thermal dust emission in a sample of 52 z ∼ 6 galaxies observed by the ALMA Lensing Cluster Survey. Methods. We perform our analysis using LINESTACKER, stacking both [C II] and continuum emission. The target sample is selected from multiple catalogues, and the sample galaxies have spectroscopic redshift or low-uncertainty photometric redshifts (σz < 0.02) in nine galaxy clusters. Source properties of the target galaxies are either extracted from the literature or computed using spectral energy distribution fitting. Both weighted-average and median stacking are used, on both the full sample and three sub-samples. Results. Our analyses find no detection of either [C II] or continuum. An upper limit on L[CII] is derived, implying that [C II] remains marginally consistent for low-SFR z > 6 galaxies but likely is under-luminous compared to the local L[CII]-SFR relationship. We discuss potential biases and possible physical effects that may be the cause of the non-detection. Further, the upper limit on the dust continuum implies that less than half of the star formation is obscured.
Adaptive Low-Rank Regularization with Damping Sequences to Restrict Lazy Weights in Deep Networks

Overfitting is one of the critical problems in deep neural networks. Many regularization schemes try to prevent overfitting blindly. However, they decrease the convergence speed of training algorithms. Adaptive regularization schemes can solve overfitting more intelligently. They usually do not affect the entire network weights. This paper detects a subset of the weighting layers that cause overfitting. The overfitting recognizes by matrix and tensor condition numbers. An adaptive regularization scheme entitled Adaptive Low-Rank (ALR) is proposed that converges a subset of the weighting layers to their Low-Rank Factorization (LRF). It happens by minimizing a new Tikhonov-based loss function. ALR also encourages lazy weights to contribute to the regularization when epochs grow up. It uses a damping sequence to increment layer selection likelihood in the last generations. Thus before falling the training accuracy, ALR reduces the lazy weights and regularizes the network substantially. The experimental results show that ALR regularizes the deep networks well with high training speed and low resource usage.
Effective magnetic coupling with strong spin frustration in (Ph3MeP+)(C60˙−) and reversible C60˙− dimerization in (Ph3MeP+)(C60˙−)·C6H5CN. Effect of solvent on structure and properties

Solvent free and solvent containing salts of the C60˙− radical anions with the triphenylmethylphosphonium cations, (Ph3MeP+)(C60˙−) (1) and (Ph3MeP+)(C60˙−)·C6H5CN (2), have been obtained. The salts show different structures and physical properties. Complex 1 has double chains from C60˙− with triangular fullerene arrangement and short interfullerene center-to-center distances of 10.079(1)–10.103(1) A. No dimerization is observed in 1 preserving unpaired spins down to 1.9 K and providing magnetic moment of 1.68 μB at 300 K. Weiss temperature of Θ = −60 K indicates a strong antiferromagnetic coupling of spins. In spite of that, no magnetic ordering is observed down to 1.9 K showing that 1 has strong frustration of spins (f = Θ/TN > 30) due to triangular fullerene lattice. Compound 2 additionally contains the C6H5CN solvent molecules and has a layered structure with fullerene hexagons accommodating the Ph3MeP+ cations and C6H5CN solvent molecules in the center. Interfullerene center-to-center distances vary in the fullerene layers in the 9.921(2)–10.066(2) A range. Fullerenes dimerize reversibly in pairs with a shortest interfullerene center-to-center distance of 9.921(2) A below 220 K to form diamagnetic singly-bonded (C60−)2 dimers.
Extraordinary 
π
-electron superconductivity emerging from a quantum spin liquid

S. Imajo1,2,∗, S. Sugiura, H. Akutsu, Y. Kohama, T. Isono, T. Terashima, K. Kindo, S. Uji, and Y. Nakazawa Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan Institute for Solid State Physics, University of Tokyo, Kashiwa, Chiba 277-8581, Japan National Institute for Materials Science, Tsukuba, Ibaraki 305-0003, Japan Present address: Institute for Materials Research, Tohoku University, Sendai 980-8577, Japan
The GALANTE photometric survey of the northern Galactic plane: project description and pipeline

The GALANTE optical photometric survey is observing the northern Galactic plane and some adjacent regions using seven narrow- and intermediate-filters, covering a total of 1618 deg2. The survey has been designed with multiple exposure times and at least two different air masses per field to maximize its photometric dynamic range, comparable to that of Gaia, and ensure the accuracy of its photometric calibration. The goal is to reach at least 1 per cent accuracy and precision in the seven bands for all stars brighter than AB magnitude 17 while detecting fainter stars with lower values of the signal-to-noise ratio. The main purposes of GALANTE are the identification and study of extinguished O+B+WR stars, the derivation of their extinction characteristics, and the cataloguing of F and G stars in the solar neighbourhood. Its data will be also used for a variety of other stellar studies and to generate a high-resolution continuum-free map of the Hα emission in the Galactic plane. We describe the techniques and the pipeline that are being used to process the data, including the basis of an innovative calibration system based on Gaia DR2 and 2MASS photometry.