arXiv subjects

Ziming Liu

Publications and source records attributed to Ziming Liu.

At least 73 records · Page 4Linked to original sources

arXiv2024

HiFAST: An HI Data Calibration and Imaging Pipeline for FAST III. Standing Wave Removal

The standing waves existed in radio telescope data are primarily due to reflections among the instruments, which significantly impact the spectrum quality of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes. Over a ten-second period, the phases shift by 18$^{\circ}$ while the amplitudes fluctuate by 6 mK. Thus, we developed the fast Fourier transform (FFT) filter method to eliminate these standing waves for every individual spectrum. The FFT filter can decrease the root mean square (RMS) from 3.2 to 1.15 times the theoretical estimate. Compared to other methods such as sine fitting and running median, the FFT filter achieves a median RMS of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%. Additionally, the FFT filter method avoids the flux loss issue encountered with some other methods. The FFT is also efficient in detecting harmonic radio frequency interference (RFI). In the FAST data, we identified three distinct types of harmonic RFI, each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1, 0.5, and 0.37 MHz, respectively. The FFT filter, proven as the most effective method, is integrated into the HI data calibration and imaging pipeline for FAST (HiFAST, https://hifast.readthedocs.io).

astro-ph.IM↗

arXiv2024

How Diffusion Models Learn to Factorize and Compose

Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set, demonstrating the ability to \textit{compositionally generalize}. Nonetheless, the precise mechanism of compositionality and how it is acquired through training remains elusive. Inspired by cognitive neuroscientific approaches, we consider a highly reduced setting to examine whether and when diffusion models learn semantically meaningful and factorized representations of composable features. We performed extensive controlled experiments on conditional Denoising Diffusion Probabilistic Models (DDPMs) trained to generate various forms of 2D Gaussian bump images. We found that the models learn factorized but not fully continuous manifold representations for encoding continuous features of variation underlying the data. With such representations, models demonstrate superior feature compositionality but limited ability to interpolate over unseen values of a given feature. Our experimental results further demonstrate that diffusion models can attain compositionality with few compositional examples, suggesting a more efficient way to train DDPMs. Finally, we connect manifold formation in diffusion models to percolation theory in physics, offering insight into the sudden onset of factorized representation learning. Our thorough toy experiments thus contribute a deeper understanding of how diffusion models capture compositional structure in data.

cs.AI↗

arXiv2024

Deep extragalactic HI survey of the COSMOS field with FAST

We present a deep HI survey at L-band conducted with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) over the COSMOS field. This survey is strategically designed to overlap with the MIGHTEE COSMOS field, aiming to combine the sensitivity of the FAST and high-resolution of the MeerKAT. We observed the field with FAST for approximately 11 hours covering $\sim$2 square degrees, and reduced the raw data to HI spectral cubes over the frequency range 1310-1420 MHz. The FAST-HI data reach a median 3$σ$ column density of $N_{\rm HI}\sim2\times10^{17}$ cm$^{-2}$ over a 5 km s$^{-1}$ channel width, allowing for studies of the distribution of HI gas in various environments, such as in galaxies, the Circum-Galactic Medium (CGM) and Intergalactic Medium (IGM). We visually searched the spectral cubes for HI sources, and found a total of 80 HI detections, of which 56 have been cross-matched with the MIGHTEE-HI catalogue. With the cross-matched sources, we compare their HI masses and find that the total HI mass fraction in the IGM and CGM surrounding the galaxy pairs is statistically higher than the HI fraction surrounding the isolated galaxies by a difference of 13$\pm$4%, indicating that the CGM and IGM associated with interacting systems are richer in neutral hydrogen compared to those around isolated galaxies in the local Universe. We also describe several FAST-MeerKAT synergy projects, highlighting the full potential of exploiting both single-dish and interferometric observations to study the distribution and evolution of the diffuse HI gas.

astro-ph.GA↗

arXiv2024

To Shelter or Not To Shelter: Exploring the Influence of Different Modalities in Virtual Reality on Individuals' Tornado Mitigation Behaviors

Timely and adequate risk communication before natural hazards can reduce losses from extreme weather events and provide more resilient disaster preparedness. However, existing natural hazard risk communications have been abstract, ineffective, not immersive, and sometimes counterproductive. The implementation of virtual reality (VR) for natural hazard risk communication presents a promising alternative to the existing risk communication system by offering immersive and engaging experiences. However, it is still unknown how different modalities in VR could affect individuals' mitigation behaviors related to incoming natural hazards. In addition, it is also not clear how the repetitive risk communication of different modalities in the VR system leads to the effect of risk habituation. To fill the knowledge gap, we developed a VR system with a tornado risk communication scenario and conducted a mixed-design human subject experiment (N = 24). We comprehensively investigated our research using both quantitative and qualitative results.

cs.HC↗

arXiv2024

Observation of HI around three satellite galaxies of the M31 with the FAST: Andromeda II, NGC 205, and NGC 185

With the exceptional sensitivity of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conducted observations of the neutral hydrogen (HI) in the circumgalactic medium of Andromeda's (M31) satellite galaxies, specifically Andromeda II, NGC 205, and NGC 185. Initially, three drift scans were executed for these satellites, with a detection limit of $4\times10^{18}$ cm$^{-2}$ ( approximately $1.88\times10^3 M_{\odot}$ of HI mass), followed by a more in-depth scan of a specific region. We discovered a C-shaped HI arc structure sharing a position and line-of-sight velocity similar to a stellar ring structure around Andromeda II, hinting at a potential connection with Andromeda II. In the context of NGC 205, we identified two mass concentrations in the northeast direction, which could be indicative of tidal streams resulting from the interaction between this galaxy and M31. These new lumps discovered could be very helpful in solving the missing interstellar medium (ISM) problem for NGC 205. Observations regarding NGC 185 are consistent with previous studies, and we did not detect any additional HI material around this galaxy. These observational results enhance our understanding of the evolution of these satellite galaxies and provide insight into their historical interactions with the galaxy M31.

astro-ph.GA↗

arXiv2024

HiFAST : An HI Data Calibration and Imaging Pipeline for FAST II. Flux Density Calibration

Accurate flux density calibration is essential for precise analysis and interpretation of observations across different observation modes and instruments. In this research, we firstly introduce the flux calibration model incorporated in HIFAST pipeline, designed for processing HI 21-cm spectra. Furthermore, we investigate different calibration techniques and assess the dependence of the gain parameter on the time and environmental factors. A comparison is carried out in various observation modes (e.g. tracking and scanning modes) to determine the flux density gain ($G$), revealing insignificant discrepancies in $G$ among different methods. Long-term monitoring data shows a linear correlation between $G$ and atmospheric temperature. After subtracting the $G$--Temperature dependence, the dispersion of $G$ is reduced to $<$3% over a one-year time scale. The stability of the receiver response of FAST is considered sufficient to facilitate HI observations that can accommodate a moderate error in flux calibration (e.g., $>\sim5\%$) when utilizing a constant $G$ for calibration purposes. Our study will serve as a useful addition to the results provided by Jiang et al. (2020). Detailed measurement of $G$ for the 19 beams of FAST, covering the frequency range 1000 MHz -- 1500 MHz can be found on the HIFAST homepage: https://hifast.readthedocs.io/fluxgain.

astro-ph.IM↗

arXiv2024

Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics and transferring the learned model to unseen ones. However, they neglect the integrity of local and global features. Although the use of the attention structure will accurately locate local features, especially objects, it will significantly lose its integrity, and the relationship between classes will also be affected. Rough processing of global features will also directly affect comprehensiveness. This neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and comprehensive visual-semantic framework for MLZSL, dubbed Epsilon, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. In terms of spatial information, we achieve effective refinement by group aggregating image features into several semantic prompts. It can aggregate semantic information rather than class information, preserving the correlation between semantics. In terms of global semantics, we use global forward propagation to collect as much information as possible to ensure that semantics are not omitted. Experiments on large-scale MLZSL benchmark datasets NUS-Wide and Open-Images-v4 demonstrate that the proposed Epsilon outperforms other state-of-the-art methods with large margins.

cs.CV↗

arXiv2024

KAN 2.0: Kolmogorov-Arnold Networks Meet Science

A major challenge of AI + Science lies in their inherent incompatibility: today's AI is primarily based on connectionism, while science depends on symbolism. To bridge the two worlds, we propose a framework to seamlessly synergize Kolmogorov-Arnold Networks (KANs) and science. The framework highlights KANs' usage for three aspects of scientific discovery: identifying relevant features, revealing modular structures, and discovering symbolic formulas. The synergy is bidirectional: science to KAN (incorporating scientific knowledge into KANs), and KAN to science (extracting scientific insights from KANs). We highlight major new functionalities in the pykan package: (1) MultKAN: KANs with multiplication nodes. (2) kanpiler: a KAN compiler that compiles symbolic formulas into KANs. (3) tree converter: convert KANs (or any neural networks) to tree graphs. Based on these tools, we demonstrate KANs' capability to discover various types of physical laws, including conserved quantities, Lagrangians, symmetries, and constitutive laws.

cs.LG↗

arXiv2024

AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference

Large deep learning models have achieved impressive performance across a range of applications. However, their large memory requirements, including parameter memory and activation memory, have become a significant challenge for their practical serving. While existing methods mainly address parameter memory, the importance of activation memory has been overlooked. Especially for long input sequences, activation memory is expected to experience a significant exponential growth as the length of sequences increases. In this approach, we propose AutoChunk, an automatic and adaptive compiler system that efficiently reduces activation memory for long sequence inference by chunk strategies. The proposed system generates chunk plans by optimizing through multiple stages. In each stage, the chunk search pass explores all possible chunk candidates and the chunk selection pass identifies the optimal one. At runtime, AutoChunk employs code generation to automatically apply chunk strategies. The experiments demonstrate that AutoChunk can reduce over 80\% of activation memory while maintaining speed loss within 10%, extend max sequence length by 3.2x to 11.7x, and outperform state-of-the-art methods by a large margin.

cs.PF↗

arXiv2024

Survival of the Fittest Representation: A Case Study with Modular Addition

When a neural network can learn multiple distinct algorithms to solve a task, how does it "choose" between them during training? To approach this question, we take inspiration from ecology: when multiple species coexist, they eventually reach an equilibrium where some survive while others die out. Analogously, we suggest that a neural network at initialization contains many solutions (representations and algorithms), which compete with each other under pressure from resource constraints, with the "fittest" ultimately prevailing. To investigate this Survival of the Fittest hypothesis, we conduct a case study on neural networks performing modular addition, and find that these networks' multiple circular representations at different Fourier frequencies undergo such competitive dynamics, with only a few circles surviving at the end. We find that the frequencies with high initial signals and gradients, the "fittest," are more likely to survive. By increasing the embedding dimension, we also observe more surviving frequencies. Inspired by the Lotka-Volterra equations describing the dynamics between species, we find that the dynamics of the circles can be nicely characterized by a set of linear differential equations. Our results with modular addition show that it is possible to decompose complicated representations into simpler components, along with their basic interactions, to offer insight on the training dynamics of representations.

cs.LG↗

arXiv2024

Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach

This article uses machine learning (ML) and explainable artificial intelligence (XAI) techniques to investigate the relationship between nutritional status and mortality rates associated with Alzheimers disease (AD). The Third National Health and Nutrition Examination Survey (NHANES III) database is employed for analysis. The random forest model is selected as the base model for XAI analysis, and the Shapley Additive Explanations (SHAP) method is used to assess feature importance. The results highlight significant nutritional factors such as serum vitamin B12 and glycated hemoglobin. The study demonstrates the effectiveness of random forests in predicting AD mortality compared to other diseases. This research provides insights into the impact of nutrition on AD and contributes to a deeper understanding of disease progression.

cs.LG↗

arXiv2024

Using a Convolutional Neural Network and Explainable AI to Diagnose Dementia Based on MRI Scans

As the number of dementia patients rises, the need for accurate diagnostic procedures rises as well. Current methods, like using an MRI scan, rely on human input, which can be inaccurate. However, the decision logic behind machine learning algorithms and their outputs cannot be explained, as most operate in black-box models. Therefore, to increase the accuracy of diagnosing dementia through MRIs, a convolution neural network has been developed and trained using an open-source database of 6400 MRI scans divided into 4 dementia classes. The model, which attained a 98 percent validation accuracy, was shown to be well fit and able to generalize to new data. Furthermore, to aid in the visualization of the model output, an explainable AI algorithm was developed by visualizing the outputs of individual filters in each convolution layer, which highlighted regions of interest in the scan. These outputs do a great job of identifying the image features that contribute most to the model classification, thus allowing users to visualize and understand the results. Altogether, this combination of the convolution neural network and explainable AI algorithm creates a system that can be used in the medical field to not only aid in the proper classification of dementia but also allow everyone involved to visualize and understand the results.

eess.IV↗

arXiv2024

How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator

How do transformers model physics? Do transformers model systems with interpretable analytical solutions, or do they create "alien physics" that are difficult for humans to decipher? We take a step in demystifying this larger puzzle by investigating the simple harmonic oscillator (SHO), $\ddot{x}+2γ\dot{x}+ω_0^2x=0$, one of the most fundamental systems in physics. Our goal is to identify the methods transformers use to model the SHO, and to do so we hypothesize and evaluate possible methods by analyzing the encoding of these methods' intermediates. We develop four criteria for the use of a method within the simple testbed of linear regression, where our method is $y = wx$ and our intermediate is $w$: (1) Can the intermediate be predicted from hidden states? (2) Is the intermediate's encoding quality correlated with model performance? (3) Can the majority of variance in hidden states be explained by the intermediate? (4) Can we intervene on hidden states to produce predictable outcomes? Armed with these two correlational (1,2), weak causal (3) and strong causal (4) criteria, we determine that transformers use known numerical methods to model trajectories of the simple harmonic oscillator, specifically the matrix exponential method. Our analysis framework can conveniently extend to high-dimensional linear systems and nonlinear systems, which we hope will help reveal the "world model" hidden in transformers.

cs.LG↗

arXiv2024

A Resource Model For Neural Scaling Law

Neural scaling laws characterize how model performance improves as the model size scales up. Inspired by empirical observations, we introduce a resource model of neural scaling. A task is usually composite hence can be decomposed into many subtasks, which compete for resources (measured by the number of neurons allocated to subtasks). On toy problems, we empirically find that: (1) The loss of a subtask is inversely proportional to its allocated neurons. (2) When multiple subtasks are present in a composite task, the resources acquired by each subtask uniformly grow as models get larger, keeping the ratios of acquired resources constants. We hypothesize these findings to be generally true and build a model to predict neural scaling laws for general composite tasks, which successfully replicates the neural scaling law of Chinchilla models reported in arXiv:2203.15556. We believe that the notion of resource used in this paper will be a useful tool for characterizing and diagnosing neural networks.

cs.LG↗

arXiv2024

OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration

Integrable partial differential equation (PDE) systems are of great interest in natural science, but are exceedingly rare and difficult to discover. To solve this, we introduce OptPDE, a first-of-its-kind machine learning approach that Optimizes PDEs' coefficients to maximize their number of conserved quantities, $n_{\rm CQ}$, and thus discover new integrable systems. We discover four families of integrable PDEs, one of which was previously known, and three of which have at least one conserved quantity but are new to the literature to the best of our knowledge. We investigate more deeply the properties of one of these novel PDE families, $u_t = (u_x+a^2u_{xxx})^3$. Our paper offers a promising schema of AI-human collaboration for integrable system discovery: machine learning generates interpretable hypotheses for possible integrable systems, which human scientists can verify and analyze, to truly close the discovery loop.

cs.LG↗

arXiv2024

Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?

Diffusion models are capable of impressive feats of image generation with uncommon juxtapositions such as astronauts riding horses on the moon with properly placed shadows. These outputs indicate the ability to perform compositional generalization, but how do the models do so? We perform controlled experiments on conditional DDPMs learning to generate 2D spherical Gaussian bumps centered at specified $x$- and $y$-positions. Our results show that the emergence of semantically meaningful latent representations is key to achieving high performance. En route to successful performance over learning, the model traverses three distinct phases of latent representations: (phase A) no latent structure, (phase B) a 2D manifold of disordered states, and (phase C) a 2D ordered manifold. Corresponding to each of these phases, we identify qualitatively different generation behaviors: 1) multiple bumps are generated, 2) one bump is generated but at inaccurate $x$ and $y$ locations, 3) a bump is generated at the correct $x$ and y location. Furthermore, we show that even under imbalanced datasets where features ($x$- versus $y$-positions) are represented with skewed frequencies, the learning process for $x$ and $y$ is coupled rather than factorized, demonstrating that simple vanilla-flavored diffusion models cannot learn efficient representations in which localization in $x$ and $y$ are factorized into separate 1D tasks. These findings suggest the need for future work to find inductive biases that will push generative models to discover and exploit factorizable independent structures in their inputs, which will be required to vault these models into more data-efficient regimes.

cs.LG↗

arXiv2024

FEASTS Combined with Interferometry (I): Overall Properties of Diffuse HI and Implications for Gas Accretion in Nearby Galaxies

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion.

astro-ph.GA↗

arXiv2024

HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

In recent times, the emergence of Large Language Models (LLMs) has resulted in increasingly larger model size, posing challenges for inference on low-resource devices. Prior approaches have explored offloading to facilitate low-memory inference but often suffer from efficiency due to I/O bottlenecks. To achieve low-latency LLMs inference on resource-constrained devices, we introduce HeteGen, a novel approach that presents a principled framework for heterogeneous parallel computing using CPUs and GPUs. Based on this framework, HeteGen further employs heterogeneous parallel computing and asynchronous overlap for LLMs to mitigate I/O bottlenecks. Our experiments demonstrate a substantial improvement in inference speed, surpassing state-of-the-art methods by over 317% at most.

cs.PF↗