Miracle-dz/UniFAR-FTUs huggingface dataset:
The Anelastic Attenuation of Surface Waves Beneath Southwestern Arabia and the Southern Red Sea

The seismic attenuation coefficients of surface waves in southwestern Arabian Peninsula and the southern Red Sea were inves- tigated using the station ratio method. Love and Rayleigh waves data recorded by two GSN stations RAYN and ATD are used to compute Love and Rayleigh waves attenuation coefficients (γ L and γ R ), and Love and Rayleigh waves internal friction (Quality factors, Q L and Q R ) in the region between the two GSN seismic stations RAYN and ATD. RAYN lies at the center of Arabia, while ATD is located on the western coast of the southern Red Sea on the Afar depression. γ R is found to vary from a low of 4.03 × 10 -4 km -1 at 27 s and a high of 22.9 × 10 -4 km -1 at 44 s. γ L , on the other hand, varies between a mini- mum of -2.28 × 10 -4 km -1 at 6 s and a maximum of 10.81 × 10 -4 km -1 at 23 s. The average quality factors Q R and Q L for the upper crust are estimated to be 100 and 160, respectively. Surface wave attenuation below the upper crust is extremely high. The high attenua- tion of the crust and upper mantle may be explained in the frame work of the ongoing tectonic process of the Red Sea and the Afar depres- sion. High temperature associated with partial melting beneath the crust can result in increasing the attenuation which is observed at long periods, while at short periods (less than 27 s), the level of attenuation is less. However, the attenuation is remarkably high. The results of this confirm previous indications that the Arabian plate, unlike other stable regions, has unusually higher attenuation characteristics for seismic waves in the crust and upper mantle comparable to many tectonically active regions of the world.
Transverse spin selectivity in helical nanofibers prepared without any chiral molecule

In the last decade, chirality-induced spin selectivity (CISS) has been attracting extensive interest. However, there still exists a large gap between experiments and quantitative theoretical results, and the microscopic mechanism of CISS, especially transverse CISS where electrons are injected perpendicular to the helix axis of chiral molecules, remains elusive. Here, we address these issues by performing a combined experimental and theoretical study on conducting polyaniline helical nanofibers which are synthesized in the absence of any chiral species. Large spin polarization is measured in both left- and right-handed nanofibers for electrons injected perpendicular to their helix axis, which is comparable to the value of parallel electron injection in other chiral molecules, and it will be reversed by switching the handedness between two enantiomers. We develop a theoretical model with extremely weak spin-orbit coupling arising exclusively from electron propagation between neighboring polyanilines and the numerical results are quantitatively consistent with the experimental data. Our results demonstrate that the supramolecular handedness is sufficient for spin-selective electron transmission in chiral molecules assembled from achiral monomers and the mechanism of transverse CISS is revealed.
Probing the Creativity of Large Language Models: Can models produce divergent semantic association?

Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content. The present study aims to investigate the creative thinking of large language models through a cognitive perspective. We utilize the divergent association task (DAT), an objective measurement of creativity that asks models to generate unrelated words and calculates the semantic distance between them. We compare the results across different models and decoding strategies. Our findings indicate that: (1) When using the greedy search strategy, GPT-4 outperforms 96% of humans, while GPT-3.5-turbo exceeds the average human level. (2) Stochastic sampling and temperature scaling are effective to obtain higher DAT scores for models except GPT-4, but face a trade-off between creativity and stability. These results imply that advanced large language models have divergent semantic associations, which is a fundamental process underlying creativity.
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in generating images from short, single-object descriptions, these models often struggle to faithfully capture all the nuanced details within longer and more elaborate textual inputs. In response, we present a novel approach leveraging Large Language Models (LLMs) to extract critical components from text prompts, including bounding box coordinates for foreground objects, detailed textual descriptions for individual objects, and a succinct background context. These components form the foundation of our layout-to-image generation model, which operates in two phases. The initial Global Scene Generation utilizes object layouts and background context to create an initial scene but often falls short in faithfully representing object characteristics as specified in the prompts. To address this limitation, we introduce an Iterative Refinement Scheme that iteratively evaluates and refines box-level content to align them with their textual descriptions, recomposing objects as needed to ensure consistency. Our evaluation on complex prompts featuring multiple objects demonstrates a substantial improvement in recall compared to baseline diffusion models. This is further validated by a user study, underscoring the efficacy of our approach in generating coherent and detailed scenes from intricate textual inputs.
Odd entanglement entropy in boundary conformal field theories and holographic moving mirrors

In this article, we investigate the entanglement structure of bipartite mixed states in (1+1)-dimensional boundary conformal field theories (BCFT$_2$s) through the odd entanglement entropy (OEE) by employing an appropriate replica technique. In this regard we compare our results with the bulk entanglement wedge cross section (EWCS) for AdS$_3$ geometries with an end-of-the-world brane. We observe consistent extension of the holographic duality between the difference of the OEE and the entanglement entropy (EE) with the bulk EWCS, in the framework of AdS$_3$/BCFT$_2$ holography. Furthermore, we also extend our computations to the holographic moving mirror models where the Hawking radiation from eternal and evaporating black holes may be simulated depending on certain mirror profiles, and find consistent extension of the aforementioned holographic duality.
Understanding Fairness Surrogate Functions in Algorithmic Fairness

It has been observed that machine learning algorithms exhibit biased predictions against certain population groups. To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem. However, it is intriguing in previous work that such fairness surrogate functions may yield unfair results and high instability. In this work, in order to deeply understand them, taking a widely used fairness definition--demographic parity as an example, we show that there is a surrogate-fairness gap between the fairness definition and the fairness surrogate function. Also, the theoretical analysis and experimental results about the gap motivate us that the fairness and stability will be affected by the points far from the decision boundary, which is the large margin points issue investigated in this paper. To address it, we propose the general sigmoid surrogate to simultaneously reduce both the surrogate-fairness gap and the variance, and offer a rigorous fairness and stability upper bound. Interestingly, the theory also provides insights into two important issues that deal with the large margin points as well as obtaining a more balanced dataset are beneficial to fairness and stability. Furthermore, we elaborate a novel and general algorithm called Balanced Surrogate, which iteratively reduces the gap to mitigate unfairness. Finally, we provide empirical evidence showing that our methods consistently improve fairness and stability while maintaining accuracy comparable to the baselines in three real-world datasets.
Data Contamination Through the Lens of Time

Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks. Since LLMs train on wide swaths of the internet, this practice raises concerns of data contamination, i.e., evaluating on examples that are explicitly or implicitly included in the training data. Data contamination remains notoriously challenging to measure and mitigate, even with partial attempts like controlled experimentation of training data, canary strings, or embedding similarities. In this work, we conduct the first thorough longitudinal analysis of data contamination in LLMs by using the natural experiment of training cutoffs in GPT models to look at benchmarks released over time. Specifically, we consider two code/mathematical problem-solving datasets, Codeforces and Project Euler, and find statistically significant trends among LLM pass rate vs. GitHub popularity and release date that provide strong evidence of contamination. By open-sourcing our dataset, raw results, and evaluation framework, our work paves the way for rigorous analyses of data contamination in modern models. We conclude with a discussion of best practices and future steps for publicly releasing benchmarks in the age of LLMs that train on webscale data.
A decoder-only foundation model for time-series forecasting

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.
3D Structure-guided Network for Tooth Alignment in 2D Photograph

Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions), affecting both masticatory function and aesthetics. However, orthodontic treatment often involves complex, lengthy procedures. As such, generating a 2D photograph depicting aligned teeth prior to orthodontic treatment is crucial for effective dentist-patient communication and, more importantly, for encouraging patients to accept orthodontic intervention. In this paper, we propose a 3D structure-guided tooth alignment network that takes 2D photographs as input (e.g., photos captured by smartphones) and aligns the teeth within the 2D image space to generate an orthodontic comparison photograph featuring aesthetically pleasing, aligned teeth. Notably, while the process operates within a 2D image space, our method employs 3D intra-oral scanning models collected in clinics to learn about orthodontic treatment, i.e., projecting the pre- and post-orthodontic 3D tooth structures onto 2D tooth contours, followed by a diffusion model to learn the mapping relationship. Ultimately, the aligned tooth contours are leveraged to guide the generation of a 2D photograph with aesthetically pleasing, aligned teeth and realistic textures. We evaluate our network on various facial photographs, demonstrating its exceptional performance and strong applicability within the orthodontic industry.
Role of magnetic ions in the thermal Hall effect of the paramagnetic insulator TmVO$_{4}$

In a growing number of materials, phonons have been found to generate a thermal Hall effect, but the underlying mechanism remains unclear. Inspired by previous studies that revealed the importance of Tb$^{3+}$ ions in generating the thermal Hall effect of Tb$_{2}$Ti$_{2}$O$_{7}$, we investigated the role of Tm$^{3+}$ ions in TmVO$_{4}$, a paramagnetic insulator with a different crystal structure. We observe a negative thermal Hall conductivity in TmVO$_{4}$ with a magnitude such that the Hall angle, $|\kappa_{xy}$/$\kappa_{xx}|$, is approximately 1 x 10$^{-3}$ at $H$ = 15 T and $T$ = 20 K, typical for a phonon-generated thermal Hall effect. In contrast to the negligible $\kappa_{xy}$ found in Y$_{2}$Ti$_{2}$O$_{7}$, we observe a negative $\kappa_{xy}$ in YVO$_{4}$ with a Hall angle of magnitude comparable to that of TmVO$_{4}$. This shows that the Tm$^{3+}$ ions are not essential for the thermal Hall effect in this family of materials. Interestingly, at an intermediate Y concentration of 30 % in Tm$_{1-x}$Y$_{x}$VO$_{4}$, $\kappa_{xy}$ was found to have a positive sign, pointing to the possible importance of impurities in the thermal Hall effect of phonons.
Algorithmic Pulsar Timer for Binaries

Pulsar timing is a powerful tool that, by accounting for every rotation of a pulsar, precisely measures the spin frequency, spin frequency derivatives, astrometric position, binary parameters when applicable, properties of the interstellar medium, and potentially general relativistic effects. Typically, this process demands fairly stringent scheduling requirements for monitoring observations as well as deep domain knowledge to “phase connect” the timing data. We present an algorithm that automates the pulsar-timing process for binary pulsars, whose timing solutions have an additional level of complexity, although the algorithm works for isolated pulsars as well. Using the statistical F-test and the quadratic dependence of the reduced χ 2 near a minimum, the global rotation count of a pulsar can be determined efficiently and systematically. We have used our algorithm to establish timing solutions for two newly discovered binary pulsars, PSRs J1748−2446aq and J1748−2446at, in the globular cluster Terzan 5, using ∼70 Green Bank Telescope observations from the last 13 yr.
Transfer of mass‐independent fractionation in ozone to other oxygen‐containing radicals in the atmosphere

Mass‐independent fractionation (MIF) of ozone has been observed in both the troposphere and stratosphere. Because ozone is a photochemically active species, its MIF signature can be imparted to other atmospheric molecules. Using a photochemical equilibrium model for short‐lived radical species, I have computed the expected MIF for typical mid‐latitude conditions. The model accounts for about ∼70% of recent measurements of Δ177O for H2O2 in rainwater, and predicts large MIF for NOx and ClO species (∼40–70‰), and their products (ClONO2 and HNO3). Furthermore, in the stratosphere oxygen exchange reactions between OH and NOx yield OH with Δ17O from 2 to 45‰. Stratospheric water produced during H abstraction by OH would be similarly mass‐independently fractionated. In the troposphere rapid exchange between OH and H2O erases any MIF signature in OH. These model results depend on several O exchange reactions with unknown activation energies or with rate coefficients known only as upper limits.
Homology and K-theory of dynamical systems III. Beyond stably disconnected Smale spaces

We study homological invariants of \'etale groupoids arising from Smale spaces, continuing on our previous work, but going beyond the stably disconnected case by incorporating resolutions in the space direction. We show that the homology groups defined by Putnam are isomorphic to the Crainic-Moerdijk groupoid homology with integer coefficients. We also show that the K-groups of C*-algebras of stable and unstable equivalence relations have finite rank. For unstably disconnected Smale spaces, we provide a cohomological spectral sequence whose second page is the (stable) homology groups, and converges to the K-groups of the unstable C*-algebra.
Pure Exploration in Asynchronous Federated Bandits

We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server. To enhance the robustness against latency and unavailability of agents that are common in practice, we propose the first federated asynchronous multi-armed bandit and linear bandit algorithms for pure exploration with fixed confidence. Our theoretical analysis shows the proposed algorithms achieve near-optimal sample complexities and efficient communication costs in a fully asynchronous environment. Moreover, experimental results based on synthetic and real-world data empirically elucidate the effectiveness and communication cost-efficiency of the proposed algorithms.
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling

Deep generative models have shown tremendous success in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, the tendency to induce spurious correlations, and poor out-of-distribution extrapolation. In an effort to remedy such challenges, one can incorporate the theory of causality in deep generative modeling. Structural causal models (SCMs) describe data-generating processes and model complex causal relationships and mechanisms among variables in a system. Thus, SCMs can naturally be combined with deep generative models. Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interoperability. We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods. We focus on fundamental theory, formulations, drawbacks, datasets, metrics, and applications of causal generative models in fairness, privacy, out-of-distribution generalization, and precision medicine. We also discuss open problems and fruitful research directions for future work in the field.
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems

Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features. However, many neurons exhibit $\textit{mixed selectivity}$, i.e., they represent multiple unrelated features. A recent hypothesis proposes that features in deep networks may be represented in $\textit{superposition}$, i.e., on non-orthogonal axes by multiple neurons, since the number of possible interpretable features in natural data is generally larger than the number of neurons in a given network. Accordingly, we should be able to find meaningful directions in activation space that are not aligned with individual neurons. Here, we propose (1) an automated method for quantifying visual interpretability that is validated against a large database of human psychophysics judgments of neuron interpretability, and (2) an approach for finding meaningful directions in network activation space. We leverage these methods to discover directions in convolutional neural networks that are more intuitively meaningful than individual neurons, as we confirm and investigate in a series of analyses. Moreover, we apply the same method to three recent datasets of visual neural responses in the brain and find that our conclusions largely transfer to real neural data, suggesting that superposition might be deployed by the brain. This also provides a link with disentanglement and raises fundamental questions about robust, efficient and factorized representations in both artificial and biological neural systems.
Towards reducing hallucination in extracting information from financial reports using Large Language Models

For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions. However, extracting valuable insights from the Q\&A section has posed considerable challenges as the conventional methods such as detailed reading and note-taking lack scalability and are susceptible to human errors, and Optical Character Recognition (OCR) and similar techniques encounter difficulties in accurately processing unstructured transcript text, often missing subtle linguistic nuances that drive investor decisions. Here, we demonstrate the utilization of Large Language Models (LLMs) to efficiently and rapidly extract information from earnings report transcripts while ensuring high accuracy transforming the extraction process as well as reducing hallucination by combining retrieval-augmented generation technique as well as metadata. We evaluate the outcomes of various LLMs with and without using our proposed approach based on various objective metrics for evaluating Q\&A systems, and empirically demonstrate superiority of our method.
Understanding Contrastive Learning via Distributionally Robust Optimization

This study reveals the inherent tolerance of contrastive learning (CL) towards sampling bias, wherein negative samples may encompass similar semantics (\eg labels). However, existing theories fall short in providing explanations for this phenomenon. We bridge this research gap by analyzing CL through the lens of distributionally robust optimization (DRO), yielding several key insights: (1) CL essentially conducts DRO over the negative sampling distribution, thus enabling robust performance across a variety of potential distributions and demonstrating robustness to sampling bias; (2) The design of the temperature $\tau$ is not merely heuristic but acts as a Lagrange Coefficient, regulating the size of the potential distribution set; (3) A theoretical connection is established between DRO and mutual information, thus presenting fresh evidence for ``InfoNCE as an estimate of MI'' and a new estimation approach for $\phi$-divergence-based generalized mutual information. We also identify CL's potential shortcomings, including over-conservatism and sensitivity to outliers, and introduce a novel Adjusted InfoNCE loss (ADNCE) to mitigate these issues. It refines potential distribution, improving performance and accelerating convergence. Extensive experiments on various domains (image, sentence, and graphs) validate the effectiveness of the proposal. The code is available at \url{https://github.com/junkangwu/ADNCE}.
A Survey on Video Diffusion Models

The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are gradually superseding methods based on GANs and auto-regressive Transformers, demonstrating exceptional performance not only in image generation and editing, but also in the realm of video-related research. However, existing surveys mainly focus on diffusion models in the context of image generation, with few up-to-date reviews on their application in the video domain. To address this gap, this paper presents a comprehensive review of video diffusion models in the AIGC era. Specifically, we begin with a concise introduction to the fundamentals and evolution of diffusion models. Subsequently, we present an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks. We conduct a thorough review of the literature in these three key areas, including further categorization and practical contributions in the field. Finally, we discuss the challenges faced by research in this domain and outline potential future developmental trends. A comprehensive list of video diffusion models studied in this survey is available at https://github.com/ChenHsing/Awesome-Video-Diffusion-Models.