Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasinormal modes of gravitational perturbation for uniformly accelerated black holes

We first show that the master equations for massless perturbations of accelerating rotating black holes can be transformed into the Heun's equation. The quasinormal modes of the black holes can be easily calculated in the framework of the Heun's equation. We identify three modes for the tensor perturbations: the photon sphere modes, which reduce to the quasinormal modes of Kerr black holes when the acceleration parameter vanishes; the near-extremal modes, which branch from the first set and become dominant when the spin is near extremal; and the acceleration modes, which are closely related to the acceleration horizon. We calculate the frequency spectrum of the QNMs in various spin and acceleration parameters. We choose an angular boundary condition that keeps the angular function regular at $ \theta = 0 $ and $\pi$, which is consistent with the boundary condition of the Kerr black hole. The conical singularity caused by the acceleration influences this boundary condition. We find that the $m_0 = 1$ modes have an anomalous behavior at particular accelerations.
Detection of Extragalactic Anomalous Microwave Emission in NGC 2903 Using KVN Single-dish Observations

We present the results of the single-dish observations using the Korean VLBI Network to search for anomalous microwave emission (AME) in nearby galaxies. The targets were selected from ‘Mapping the dense molecular gas in the strongest star-forming galaxies' (MALATANG), a legacy survey project of the James Clerk Maxwell Telescope. The MALATANG galaxies are good representatives of local galaxies with enhanced nuclear activity associated with star formation and/or active galactic nuclei (AGNs), providing IR-bright galaxy samples; thus, they are good candidates for AME hosts. Combining with ancillary data, we investigated the radio–IR spectral energy distribution (SED), while searching for AME signals in five galaxies. The AME in NGC 2903 was well detected at a significant confidence level, whereas that in NGC 2146 and M82 was marginal. NGC 1068 and Arp 299 indicated no significant hints, and we provide upper limits for the AME. The best-fit SED exhibited local peaks of the AME components at higher frequencies and with stronger peak fluxes than those in previous studies. This suggested that AME originates from denser environments such as molecular clouds or photodissociation regions rather than warm neutral/ionized medium as commonly suggested by previous studies. Further, our AME-detected targets were observed to exhibit higher specific star formation rates than the other extragalactic AME hosts. Furthermore, AME favored starburst galaxies among our sample rather than AGN hosts. Consequently, this might imply that AGNs are excessively harsh environments for tiny dust to survive.
ParZC: Parametric Zero-Cost Proxies for Efficient NAS

Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks. Several studies propose the automated design of zero-cost proxies to achieve SOTA performance but require tedious searching progress. Furthermore, we identify a critical issue with current zero-cost proxies: they aggregate node-wise zero-cost statistics without considering the fact that not all nodes in a neural network equally impact performance estimation. Our observations reveal that node-wise zero-cost statistics significantly vary in their contributions to performance, with each node exhibiting a degree of uncertainty. Based on this insight, we introduce a novel method called Parametric Zero-Cost Proxies (ParZC) framework to enhance the adaptability of zero-cost proxies through parameterization. To address the node indiscrimination, we propose a Mixer Architecture with Bayesian Network (MABN) to explore the node-wise zero-cost statistics and estimate node-specific uncertainty. Moreover, we propose DiffKendall as a loss function to directly optimize Kendall's Tau coefficient in a differentiable manner so that our ParZC can better handle the discrepancies in ranking architectures. Comprehensive experiments on NAS-Bench-101, 201, and NDS demonstrate the superiority of our proposed ParZC compared to existing zero-shot NAS methods. Additionally, we demonstrate the versatility and adaptability of ParZC by transferring it to the Vision Transformer search space.
COMET: Generating Commit Messages using Delta Graph Context Representation

Commit messages explain code changes in a commit and facilitate collaboration among developers. Several commit message generation approaches have been proposed; however, they exhibit limited success in capturing the context of code changes. We propose Comet (Context-Aware Commit Message Generation), a novel approach that captures context of code changes using a graph-based representation and leverages a transformer-based model to generate high-quality commit messages. Our proposed method utilizes delta graph that we developed to effectively represent code differences. We also introduce a customizable quality assurance module to identify optimal messages, mitigating subjectivity in commit messages. Experiments show that Comet outperforms state-of-the-art techniques in terms of bleu-norm and meteor metrics while being comparable in terms of rogue-l. Additionally, we compare the proposed approach with the popular gpt-3.5-turbo model, along with gpt-4-turbo; the most capable GPT model, over zero-shot, one-shot, and multi-shot settings. We found Comet outperforming the GPT models, on five and four metrics respectively and provide competitive results with the two other metrics. The study has implications for researchers, tool developers, and software developers. Software developers may utilize Comet to generate context-aware commit messages. Researchers and tool developers can apply the proposed delta graph technique in similar contexts, like code review summarization.
Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times

Recent studies have shown that as Transformer-based language models become larger and are trained on very large amounts of data, the fit of their surprisal estimates to naturalistic human reading times degrades. The current work presents a series of analyses showing that word frequency is a key explanatory factor underlying these two trends. First, residual errors from four language model families on four corpora show that the inverse correlation between model size and fit to reading times is the strongest on the subset of least frequent words, which is driven by excessively accurate predictions of larger model variants. Additionally, training dynamics reveal that during later training steps, all model variants learn to predict rare words and that larger model variants do so more accurately, which explains the detrimental effect of both training data amount and model size on fit to reading times. Finally, a feature attribution analysis demonstrates that larger model variants are able to accurately predict rare words based on both an effectively longer context window size as well as stronger local associations compared to smaller model variants. Taken together, these results indicate that Transformer-based language models’ surprisal estimates diverge from human-like expectations due to the superhumanly complex associations they learn for predicting rare words.
Mass-Radius relationship of Strongly Magnetized Super-Chandrasekhar Anisotropic Deformed White Dwarf Stars in presence of $\gamma$-metric

The masses and radii of strongly magnetized anisotropic deformed white dwarf stars are investigated using the stellar structure equations in the parameterized $\gamma$-metric formalism. The Equation of State (EoS) of a completely degenerate relativistic electron gas in strong quantizing density-dependent magnetic field is developed. The fluid and field pressure anisotropy among the parallel and perpendicular components to the magnetic field is taken into consideration. This anisotropy in the EoS causes axisymmetric deformation of the star. We found stable solutions of deformed super-Chandrasekhar ultramassive white dwarfs. The masses of anisotropic magnetized white dwarfs at the same central density decrease monotonically with the increase in the strength of the central magnetic field, while the equatorial radii increase monotonically. This is in sharp contrast to the isotropic case where both the mass and radius increase monotonically. High magnetic field increases anisotropy and oblateness. We also see that the maximum mass and its corresponding equatorial radius both decrease as central magnetic field strength increases. We also notice that the maximum mass occurs at higher central density as the magnetic field increases. This shows that increasing magnetic field (hence increasing anisotropy) softens the EoS and makes the star more compact.
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning

In this study, we explore the influence of different observation spaces on robot learning, focusing on three predominant modalities: RGB, RGB-D, and point cloud. Through extensive experimentation on over 17 varied contact-rich manipulation tasks, conducted across two benchmarks and simulators, we have observed a notable trend: point cloud-based methods, even those with the simplest designs, frequently surpass their RGB and RGB-D counterparts in performance. This remains consistent in both scenarios: training from scratch and utilizing pretraining. Furthermore, our findings indicate that point cloud observations lead to improved policy zero-shot generalization in relation to various geometry and visual clues, including camera viewpoints, lighting conditions, noise levels and background appearance. The outcomes suggest that 3D point cloud is a valuable observation modality for intricate robotic tasks. We will open-source all our codes and checkpoints, hoping that our insights can help design more generalizable and robust robotic models.
Translation Errors Significantly Impact Low-Resource Languages in Cross-Lingual Learning

Popular benchmarks (e.g., XNLI) used to evaluate cross-lingual language understanding consist of parallel versions of English evaluation sets in multiple target languages created with the help of professional translators. When creating such parallel data, it is critical to ensure high-quality translations for all target languages for an accurate characterization of cross-lingual transfer. In this work, we find that translation inconsistencies do exist and interestingly they disproportionally impact low-resource languages in XNLI. To identify such inconsistencies, we propose measuring the gap in performance between zero-shot evaluations on the human-translated and machine-translated target text across multiple target languages; relatively large gaps are indicative of translation errors. We also corroborate that translation errors exist for two target languages, namely Hindi and Urdu, by doing a manual reannotation of human-translated test instances in these two languages and finding poor agreement with the original English labels these instances were supposed to inherit.
Utilizing Vegetation Indices as a Proxy to Characterize the Stability of a Railway Embankment in a Permafrost Region

Degrading permafrost conditions around the world are posing stability issues for infrastructure constructed on them. Railway lines have exceptionally low tolerances for differential settlements associated with permafrost degradation due to the potential for train derailments. Railway owners with tracks in permafrost regions therefore make it a priority to identify potential settlement locations so that proper maintenance or embankment stabilization measures can be applied to ensure smooth and safe operations. The extensive discontinuous permafrost zone along the Hudson Bay Railway (HBR) in Northern Manitoba, Canada, has been experiencing accelerated deterioration, resulting in differential settlements that necessitate continuous annual maintenance to avoid slow orders and operational interruptions. This paper seeks to characterize the different permafrost degradation susceptibilities present at the study site. Track geometry exceptions were compared against remotely sensed vegetation indices to establish a relationship between track quality and vegetation density. This relationship was used as a proxy for subsurface condition verified by electrical resistivity tomography. The established relationship was then used to develop a three-level degradation susceptibility chart to indicate low, moderate and high susceptibility regions. The defined susceptibility regions can be used to better allocate the limited maintenance resources and also help inform potentially long-term stabilization measures for the severely affected sections.
Robust Multi-Task Learning with Excess Risks

Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing methods use an adaptive weight updating scheme, where task weights are dynamically adjusted based on their respective losses to prioritize difficult tasks. However, these algorithms face a great challenge whenever label noise is present, in which case excessive weights tend to be assigned to noisy tasks that have relatively large Bayes optimal errors, thereby overshadowing other tasks and causing performance to drop across the board. To overcome this limitation, we propose Multi-Task Learning with Excess Risks (ExcessMTL), an excess risk-based task balancing method that updates the task weights by their distances to convergence instead. Intuitively, ExcessMTL assigns higher weights to worse-trained tasks that are further from convergence. To estimate the excess risks, we develop an efficient and accurate method with Taylor approximation. Theoretically, we show that our proposed algorithm achieves convergence guarantees and Pareto stationarity. Empirically, we evaluate our algorithm on various MTL benchmarks and demonstrate its superior performance over existing methods in the presence of label noise.
Precise Knowledge Transfer via Flow Matching

In this paper, we propose a novel knowledge transfer framework that introduces continuous normalizing flows for progressive knowledge transformation and leverages multi-step sampling strategies to achieve precision knowledge transfer. We name this framework Knowledge Transfer with Flow Matching (FM-KT), which can be integrated with a metric-based distillation method with any form (\textit{e.g.} vanilla KD, DKD, PKD and DIST) and a meta-encoder with any available architecture (\textit{e.g.} CNN, MLP and Transformer). By introducing stochastic interpolants, FM-KD is readily amenable to arbitrary noise schedules (\textit{e.g.}, VP-ODE, VE-ODE, Rectified flow) for normalized flow path estimation. We theoretically demonstrate that the training objective of FM-KT is equivalent to minimizing the upper bound of the teacher feature map or logit negative log-likelihood. Besides, FM-KT can be viewed as a unique implicit ensemble method that leads to performance gains. By slightly modifying the FM-KT framework, FM-KT can also be transformed into an online distillation framework OFM-KT with desirable performance gains. Through extensive experiments on CIFAR-100, ImageNet-1k, and MS-COCO datasets, we empirically validate the scalability and state-of-the-art performance of our proposed methods among relevant comparison approaches.
Minusformer: Improving Time Series Forecasting by Progressively Learning Residuals

In this paper, we find that ubiquitous time series (TS) forecasting models are prone to severe overfitting. To cope with this problem, we embrace a de-redundancy approach to progressively reinstate the intrinsic values of TS for future intervals. Specifically, we renovate the vanilla Transformer by reorienting the information aggregation mechanism from addition to subtraction. Then, we incorporate an auxiliary output branch into each block of the original model to construct a highway leading to the ultimate prediction. The output of subsequent modules in this branch will subtract the previously learned results, enabling the model to learn the residuals of the supervision signal, layer by layer. This designing facilitates the learning-driven implicit progressive decomposition of the input and output streams, empowering the model with heightened versatility, interpretability, and resilience against overfitting. Since all aggregations in the model are minus signs, which is called Minusformer. Extensive experiments demonstrate the proposed method outperform existing state-of-the-art methods, yielding an average performance improvement of 11.9% across various datasets.
No Need to Look Back: An Efficient and Scalable Approach for Temporal Network Representation Learning

Temporal graph representation learning (TGRL) is crucial for modeling complex, dynamic systems in real-world networks. Traditional TGRL methods, though effective, suffer from high computational demands and inference latency. This is mainly induced by their inefficient sampling of temporal neighbors by backtracking the interaction history of each node when making model inference. This paper introduces a novel efficient TGRL framework, No-Looking-Back (NLB). NLB employs a"forward recent sampling"strategy, which bypasses the need for backtracking historical interactions. This strategy is implemented using a GPU-executable size-constrained hash table for each node, recording down-sampled recent interactions, which enables rapid response to queries with minimal inference latency. The maintenance of this hash table is highly efficient, with $O(1)$ complexity. NLB is fully compatible with GPU processing, maximizing programmability, parallelism, and power efficiency. Empirical evaluations demonstrate that NLB matches or surpasses state-of-the-art methods in accuracy for link prediction and node classification across six real-world datasets. Significantly, it is 1.32-4.40 $\times$ faster in training, 1.2-7.94 $\times$ more energy efficient, and 1.97-5.02 $\times$ more effective in reducing inference latency compared to the most competitive baselines. The link to the code: https://github.com/Graph-COM/NLB.
Shortened LLaMA: A Simple Depth Pruning for Large Language Models

Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs. Width pruning reduces the size of projection weight matrices (e.g., by removing attention heads) while maintaining the number of layers. Depth pruning, in contrast, removes entire layers or blocks, while keeping the size of the remaining weights unchanged. Most current research focuses on either width-only or a blend of width and depth pruning, with little comparative analysis between the two units (width vs. depth) concerning their impact on LLM inference efficiency. In this work, we show that a simple depth pruning approach can compete with recent width pruning methods in terms of zero-shot task performance. Our pruning method boosts inference speeds, especially under memory-constrained conditions that require limited batch sizes for running LLMs, where width pruning is ineffective. We hope this work can help deploy LLMs on local and edge devices.
Role of mentorship, career conceptualization, and leadership in developing women’s physics identity and belonging

The percentage of women receiving bachelors degrees in physics in the U.S. lags well behind that of men, and women leave the major at higher rates. Achieving equity in physics will mean that women stay in physics at the same rates as men, but this will require changes in the culture and support structures. A strong sense of belonging can lead to higher retention rates so interventions meant to increase dimensions of physics identity (interest, recognition, performance, and competence) may increase persistence overall and increase women's retention differentially. We describe our model in which mentorship, an understanding of career options (career conceptualization), and leadership are inputs into the development of these dimensions of physics identity. This paper includes preliminary results from a qualitative study that aims to better understand how career conceptualization, leadership, and mentorship contribute to the development of physics identity and belonging. We report results from a survey of 15 undergraduate physics students which was followed up by interviews with 5 of those students. The students were from a small private liberal arts college in the midwest region of the U.S. and a large public university in the southeast region of the U.S. classified as a Hispanic-serving institution (HSI). With respect to mentorship, we found that it could provide critical support for students' engagement in the physics community. Leadership experiences have not previously been positioned as an important input into identity, yet we found that they helped women in physics feel more confident, contributing to their recognition of themselves as physics people. While the data on how career conceptualization contributed to the building of identity is limited, there are some connections to recognition and competence, and it will be an interesting avenue of future exploration.
M3Face: A Unified Multi-Modal Multilingual Framework for Human Face Generation and Editing

Human face generation and editing represent an essential task in the era of computer vision and the digital world. Recent studies have shown remarkable progress in multi-modal face generation and editing, for instance, using face segmentation to guide image generation. However, it may be challenging for some users to create these conditioning modalities manually. Thus, we introduce M3Face, a unified multi-modal multilingual framework for controllable face generation and editing. This framework enables users to utilize only text input to generate controlling modalities automatically, for instance, semantic segmentation or facial landmarks, and subsequently generate face images. We conduct extensive qualitative and quantitative experiments to showcase our frameworks face generation and editing capabilities. Additionally, we propose the M3CelebA Dataset, a large-scale multi-modal and multilingual face dataset containing high-quality images, semantic segmentations, facial landmarks, and different captions for each image in multiple languages. The code and the dataset will be released upon publication.
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty

We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning algorithm with improved continuous control performance thanks to its ability to mitigate the exploration-exploitation trade-off. PAC achieves this by seamlessly integrating stochastic policies and critics, creating a dynamic synergy between the estimation of critic uncertainty and actor training. The key contribution of our PAC algorithm is that it explicitly models and infers epistemic uncertainty in the critic through Probably Approximately Correct-Bayesian (PAC-Bayes) analysis. This incorporation of critic uncertainty enables PAC to adapt its exploration strategy as it learns, guiding the actor's decision-making process. PAC compares favorably against fixed or pre-scheduled exploration schemes of the prior art. The synergy between stochastic policies and critics, guided by PAC-Bayes analysis, represents a fundamental step towards a more adaptive and effective exploration strategy in deep reinforcement learning. We report empirical evaluations demonstrating PAC's enhanced stability and improved performance over the state of the art in diverse continuous control problems.