Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic Structure of Low‐Dimensional Inorganic/Organic Interfaces: Hybrid Density Functional Theory, G
0
W
0, and Electrostatic Models

First‐principles simulations of electronic properties of hybrid inorganic/organic interfaces are challenging, as common density functional theory (DFT) approximations target specific material classes like bulk semiconductors or gas‐phase molecules. Taking as a prototypical example anthracene (ANT) physisorbed on monolayer MoS2, the ability of different ab initio schemes to describe the electronic structure using semilocal and hybrid DFT is assessed. For the latter, an unconstrained three‐parameter range‐separation scheme is used. Comparisons against results from the many‐body perturbation theory indicate that properly parametrized hybrid functionals can approximate with reasonable accuracy the quasiparticle properties of both ANT and MoS2 taken by themselves. However, this is not the case for the hybrid interface, where neither functional can predict the correct‐level alignment nor provide a particularly good starting point for G 0 W 0 calculations. It is shown that nonempirically parametrized electrostatic models can accomplish the same task at negligible computational costs. Such schemes can include substrates of hybrid interfaces in good agreement with experimental data. The results indicate that currently, fully atomistic, many‐body simulations of weakly interacting hybrid systems are not worth the required computational resources. In contrast, ab initio‐parametrized effective models mimicking the environment offer a scalable alternative without compromising accuracy and predictivity.
Believability and Harmfulness Shape the Virality of Misleading Social Media Posts

Misinformation on social media presents a major threat to modern societies. While previous research has analyzed the virality across true and false social media posts, not every misleading post is necessarily equally viral. Rather, misinformation has different characteristics and varies in terms of its believability and harmfulness – which might influence its spread. In this work, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. Specifically, we analyze (and validate) a large sample of crowd-annotated social media posts from Twitter’s Birdwatch platform, on which users can rate the believability and harmfulness of misleading tweets. To address our research questions, we implement an explanatory regression model and link the crowd ratings for believability and harmfulness to the virality of misleading posts on Twitter. Our findings imply that misinformation that is (i) easily believable and (ii) not particularly harmful is associated with more viral resharing cascades. These results offer insights into how different kinds of crowd fact-checked misinformation spreads and suggest that the most viral misleading posts are often not the ones that are particularly concerning from the perspective of public safety. From a practical view, our findings may help platforms to develop more effective strategies to curb the proliferation of misleading posts on social media.
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration.
Fast Gravitational-wave Parameter Estimation without Compromises

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically differentiable, and accelerator-compatible waveforms, and gradient-based Markov Chain Monte Carlo sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub at https://github.com/kazewong/jim.
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds

We examine the relationship between the mutual information between the output model and the empirical sample and the generalization of the algorithm in the context of stochastic convex optimization. Despite increasing interest in information-theoretic generalization bounds, it is uncertain if these bounds can provide insight into the exceptional performance of various learning algorithms. Our study of stochastic convex optimization reveals that, for true risk minimization, dimension-dependent mutual information is necessary. This indicates that existing information-theoretic generalization bounds fall short in capturing the generalization capabilities of algorithms like SGD and regularized ERM, which have dimension-independent sample complexity.
Study of the Ultraviolet Behavior of an O($N$) $|\vec \phi|^6$ Theory in $d=3$ Dimensions

We study the ultraviolet (UV) behavior of an O($N$) $|\vec \phi |^6$ theory in $d=3$ spacetime dimensions, focusing on the question of the range in $N$ over which the perturbative beta function exhibits robust evidence of a UV zero in the $|\vec \phi |^6$ coupling, $g$. The four-loop $(4\ell)$ beta function is known to have a (scheme-independent) UV zero at $g=g_{_{UV,4\ell}}$, which is reliably calculable for large $N$. For our analysis we use the six-loop beta function calculated in the minimal subtraction scheme. We find that this six-loop beta function has a UV zero, $g_{_{UV,6\ell}}$, if $N>N_c$, where $N_c \simeq 796$, and we calculate $g_{_{UV,6\ell}}$. To investigate the reliability of the result in the region of $N \gtrsim N_c$, we apply three methods: (i) calculation of the fractional difference between $g_{_{UV,4\ell}}$ and $g_{_{UV,6\ell}}$, (ii) a Pad\'e approximant, and (iii) an assessment of scheme dependence. Our results provide quantitative measures of the range of $N$ over which the six-loop beta function has a UV zero and of the $1/N$ corrections to the value of $g$ at the UV zero for large but finite $N$. If one imposes a benchmark requirement that the fractional difference between $g_{_{UV,4\ell}}$ and $g_{_{UV,6\ell}}$ must be less than 15 \%, then our results show that this requirement is satisfied for $N \gtrsim 2 \times 10^3$. The possible role of nonperturbative effects is also noted.
Approximating Binary Longest Common Subsequence in Almost-Linear Time

The Longest Common Subsequence (LCS) is a fundamental string similarity measure, and computing the LCS of two strings is a classic algorithms question. A textbook dynamic programming algorithm gives an exact algorithm in quadratic time, and this is essentially best possible under plausible fine-grained complexity assumptions, so a natural problem is to find faster approximation algorithms. When the inputs are two binary strings, there is a simple 1/2-approximation in linear time: compute the longest common all-0s or all-1s subsequence. It has been open whether a better approximation is possible even in truly subquadratic time. Rubinstein and Song showed that the answer is yes under the assumption that the two input strings have equal lengths. We settle the question, generalizing their result to unequal length strings, proving that, for any ε>0, there exists δ>0 and a (1/2+δ)-approximation algorithm for binary LCS that runs in n1+ε time. As a consequence of our result and a result of Akmal and Vassilevska-Williams, for any ε>0, there exists a (1/q+δ)-approximation for LCS over q-ary strings in n1+ε time. Our techniques build on the recent work of Guruswami, He, and Li who proved new bounds for error-correcting codes tolerating deletion errors. They prove a combinatorial “structure lemma” for strings which classifies them according to their oscillation patterns. We prove and use an algorithmic generalization of this structure lemma, which may be of independent interest.
Star-Shaped Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models (DDPMs) provide the foundation for the recent breakthroughs in generative modeling. Their Markovian structure makes it difficult to define DDPMs with distributions other than Gaussian or discrete. In this paper, we introduce Star-Shaped DDPM (SS-DDPM). Its star-shaped diffusion process allows us to bypass the need to define the transition probabilities or compute posteriors. We establish duality between star-shaped and specific Markovian diffusions for the exponential family of distributions and derive efficient algorithms for training and sampling from SS-DDPMs. In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM. However, SS-DDPMs provide a simple recipe for designing diffusion models with distributions such as Beta, von Mises$\unicode{x2013}$Fisher, Dirichlet, Wishart and others, which can be especially useful when data lies on a constrained manifold. We evaluate the model in different settings and find it competitive even on image data, where Beta SS-DDPM achieves results comparable to a Gaussian DDPM. Our implementation is available at https://github.com/andrey-okhotin/star-shaped .
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Biobank Studies

Surrogate variables in electronic health records (EHR) and biobank data play an important role in biomedical studies due to the scarcity or absence of chart-reviewed gold standard labels. We develop a novel approach named SASH for {\bf S}urrogate-{\bf A}ssisted and data-{\bf S}hielding {\bf H}igh-dimensional integrative regression. It is a semi-supervised approach that efficiently leverages sizable unlabeled samples with error-prone EHR surrogate outcomes from multiple local sites, to improve the learning accuracy of the small gold-labeled data. {To facilitate stable and efficient knowledge extraction from the surrogates, our method first obtains a preliminary supervised estimator, and then uses it to assist training a regularized single index model (SIM) for the surrogates. Interestingly, through a chain of convex and properly penalized sparse regressions that approximate the SIM loss with bias-correction, our method avoids the local minima issue of the SIM training, and fully eliminates the impact of the preliminary estimator's large error. In addition, it protects individual-level information through summary-statistics-based data aggregation across the local sites, leveraging a similar idea of bias-corrected approximation for SIM. } Through simulation studies, we demonstrate that our method outperforms existing approaches on finite samples. Finally, we apply our method to develop a high dimensional genetic risk model for type II diabetes using large-scale data sets from UK and Mass General Brigham biobanks, where only a small fraction of subjects in one site has been labeled via chart reviewing.
Ice particles in maritime clouds near Tasmania

An instrumented aircraft was used to sample particles in the upper regions of small supercooled cumulus clouds in maritime air off the Tasmanian coast in May 1968. 114 clouds were studied on nine days, and it was found on average that clouds had a 50 per cent chance of containing ice particles when a summit temperature of about − 8°C was reached. Not only cloud temperature but also cloud width had a profound effect on whether ice particles were found. The cloud summit temperature at which ice particles first reached detectable concentrations in narrow clouds on a particular day was related to the ice nucleus concentration as measured in a cloud chamber, being, in general, highest on days of high ice nucleus concentration. However, in some glaciated clouds ice crystal concentrations of the order of 100 1−1 were found. These were greater by a factor of ∼ 104 than the estimated number of active ice nuclei. The process of multiplication of ice crystals may be associated with the riming of ice particles. Riming was assisted by the presence of large droplets, up to drizzle size, in these clouds. Multiplication of the number of ice crystals by a factor of 10 took place in about 8 minutes. The various stages in the glaciation process are described.
Translating Natural Language to Planning Goals with Large-Language Models

Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains. Unfortunately, recent work has also shown that LLMs are unable to perform accurate reasoning nor solve planning problems, which may limit their usefulness for robotics-related tasks. In this work, our central question is whether LLMs are able to translate goals specified in natural language to a structured planning language. If so, LLM can act as a natural interface between the planner and human users; the translated goal can be handed to domain-independent AI planners that are very effective at planning. Our empirical results on GPT 3.5 variants show that LLMs are much better suited towards translation rather than planning. We find that LLMs are able to leverage commonsense knowledge and reasoning to furnish missing details from under-specified goals (as is often the case in natural language). However, our experiments also reveal that LLMs can fail to generate goals in tasks that involve numerical or physical (e.g., spatial) reasoning, and that LLMs are sensitive to the prompts used. As such, these models are promising for translation to structured planning languages, but care should be taken in their use.
Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues

Newcomers are critical for the success and continuity of open source software (OSS) projects. To attract newcomers and facilitate their onboarding, many OSS projects recommend tasks for newcomers, such as good first issues (GFIs). Previous studies have preliminarily investigated the effects of GFIs and techniques to identify suitable GFIs. However, it is still unclear whether just recommending tasks is enough and how significant mentoring is for newcomers. To better understand mentoring in OSS communities, we analyze the resolution process of 48,402 GFIs from 964 repositories through a mix-method approach. We investigate the extent, the mentorship structures, the discussed topics, and the relevance of expert involvement. We find that ~70% of GFIs have expert participation, with each GFI usually having one expert who makes two comments. Half of GFIs will receive their first expert comment within 8.5 hours after a newcomer comment. Through analysis of the collaboration networks of newcomers and experts, we observe that community mentorship presents four types of structure: centralized mentoring, decentralized mentoring, collaborative mentoring, and distributed mentoring. As for discussed topics, we identify 14 newcomer challenges and 18 expert mentoring content. By fitting the generalized linear models, we find that expert involvement positively correlates with newcomers' successful contributions but negatively correlates with newcomers' retention. Our study manifests the status and significance of mentoring in the OSS projects, which provides rich practical implications for optimizing the mentoring process and helping newcomers contribute smoothly and suecessfully,
The Wisdom of Hindsight Makes Language Models Better Instruction Followers

Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback. The so-called algorithm, Reinforcement Learning with Human Feedback (RLHF) demonstrates impressive performance on the GPT series models. However, the underlying Reinforcement Learning (RL) algorithm is complex and requires an additional training pipeline for reward and value networks. In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner. Such an algorithm doesn't require any additional parameters except for the original language model and maximally reuses the pretraining pipeline. To achieve this, we formulate instruction alignment problem for language models as a goal-reaching problem in decision making. We propose Hindsight Instruction Relabeling (HIR), a novel algorithm for aligning language models with instructions. The resulting two-stage algorithm shed light to a family of reward-free approaches that utilize the hindsightly relabeled instructions based on feedback. We evaluate the performance of HIR extensively on 12 challenging BigBench reasoning tasks and show that HIR outperforms the baseline algorithms and is comparable to or even surpasses supervised finetuning.
Charge Density Wave Order in Superconducting Topological Insulator Nbx-Bi2Se3

Spontaneously broken symmetry in the ground state of Bi-based topological materials can lead to promising Topological superconductors, such as Cu-Bi2Se3, Sr-Bi2Se3 and Nb-Bi2Se3. In recent studies, coexistence of multiple Fermi surface features and superconductivity was found in Nb-Bi2Se3. However, the resultant mutliple Fermi surface--charge density wave has not been experimentally reported yet. In this paper, we report possible evidence of co-occurence of a charge density wave (CDW) ground state and a superconducting (SC) ground state in Nb intercalated Bi2Se3. An intercalation induced Periodic Lattice Distortion appears to help stabilize the CDW state, possibly assisting in the formation of 1D chains and rendering electronic and phonon anisotropy to this intriguing system.
Designing Robust Transformers using Robust Kernel Density Estimation

Recent advances in Transformer architectures have empowered their empirical success in a variety of tasks across different domains. However, existing works mainly focus on predictive accuracy and computational cost, without considering other practical issues, such as robustness to contaminated samples. Recent work by Nguyen et al., (2022) has shown that the self-attention mechanism, which is the center of the Transformer architecture, can be viewed as a non-parametric estimator based on kernel density estimation (KDE). This motivates us to leverage a set of robust kernel density estimation methods for alleviating the issue of data contamination. Specifically, we introduce a series of self-attention mechanisms that can be incorporated into different Transformer architectures and discuss the special properties of each method. We then perform extensive empirical studies on language modeling and image classification tasks. Our methods demonstrate robust performance in multiple scenarios while maintaining competitive results on clean datasets.
MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope

Explaining the predictions of deep neural nets has been a topic of great interest in the computer vision literature. While several gradient-based interpretation schemes have been proposed to reveal the influential variables in a neural net’s prediction, standard gradient-based interpretation frameworks have been commonly observed to lack robustness to input perturbations and flexibility for incorporating prior knowledge of sparsity and group-sparsity structures. In this work, we propose MoreauGrad as an interpretation scheme based on the classifier neural net’s Moreau envelope. We demonstrate that MoreauGrad results in a smooth and robust interpretation of a multi-layer neural network and can be efficiently computed through first-order optimization methods. Furthermore, we show that MoreauGrad can be naturally combined with L1-norm regularization techniques to output a sparse or group-sparse explanation which are prior conditions applicable to a wide range of deep learning applications. We empirically evaluate the proposed MoreauGrad scheme on standard computer vision datasets, showing the qualitative and quantitative success of the MoreauGrad approach in comparison to standard gradient-based interpretation methods 1.
Black-Box Generalization

We provide the first generalization error analysis for black-box learning through derivative-free optimization. Under the assumption of a Lipschitz and smooth unknown loss, we consider the Zeroth-order Stochastic Search (ZoSS) algorithm, that updates a $d$-dimensional model by replacing stochastic gradient directions with stochastic differences of $K+1$ perturbed loss evaluations per dataset (example) query. For both unbounded and bounded possibly nonconvex losses, we present the first generalization bounds for the ZoSS algorithm. These bounds coincide with those for SGD, and rather surprisingly are independent of $d$, $K$ and the batch size $m$, under appropriate choices of a slightly decreased learning rate. For bounded nonconvex losses and a batch size $m=1$, we additionally show that both generalization error and learning rate are independent of $d$ and $K$, and remain essentially the same as for the SGD, even for two function evaluations. Our results extensively extend and consistently recover established results for SGD in prior work, on both generalization bounds and corresponding learning rates. If additionally $m=n$, where $n$ is the dataset size, we derive generalization guarantees for full-batch GD as well.
An electron-beam based Compton scattering x-ray source for probing high-energy-density physics

The physics basis for an electron-beam based Compton scattering (ECOS) x-ray source is investigated for single-shot experiments at major high energy density facilities such as the Omega Laser Facility, National Ignition Facility, and Z pulsed power facility. A source of monoenergetic ($\delta\epsilon/\epsilon<5\%$) 10- to 50-keV x rays can be produced by scattering of a short-pulse optical laser by a 23- to 53-MeV electron beam and collimating the scattered photons. The number and spectrum of scattered photons is calculated as a function of electron packet charge, electron and laser pulse duration, laser intensity, and collision geometry. A source with greater than 10$^{10}$ photons in a 1-mm radius spot at the OMEGA target chamber center and 100-ps time resolution is plausible with the available electron gun and laser technology. Design requirements for diffraction, inelastic scattering and imaging experiments as well as opportunities for improved performance are discussed.