arXiv subjects

Liu

Publications and source records attributed to Liu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial

This tutorial investigates the convergence of statistical mechanics and learning theory, elucidating the potential enhancements in machine learning methodologies through the integration of foundational principles from physics. The tutorial delves into advanced techniques like entropy, free energy, and variational inference which are utilized in machine learning, illustrating their significant contributions to model efficiency and robustness. By bridging these scientific disciplines, we aspire to inspire newer methodologies in researches, demonstrating how an in-depth comprehension of physical systems' behavior can yield more effective and dependable machine learning models, particularly in contexts characterized by uncertainty.

cs.LG↗

arXiv2024

TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction

Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. However, many trajectory prediction models produce unreasonable trajectory samples that focus on improving diversity or accuracy while neglecting other key requirements, such as collision avoidance with the surrounding environment. In this work, we propose TrajDiffuse, a planning-based trajectory prediction method using a novel guided conditional diffusion model. We form the trajectory prediction problem as a denoising impaint task and design a map-based guidance term for the diffusion process. TrajDiffuse is able to generate trajectory predictions that match or exceed the accuracy and diversity of the SOTA, while adhering almost perfectly to environmental constraints. We demonstrate the utility of our model through experiments on the nuScenes and PFSD datasets and provide an extensive benchmark analysis against the SOTA methods.

cs.CV↗

arXiv2024

Development of an ultra-sensitive 210-micron array of KIDs for far-IR astronomy

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a proposed space observatory which will use arrays of thousands of kinetic inductance detectors (KIDs) to perform low- and moderate-resolution spectroscopy throughout the far-infrared. The detectors must have noise equivalent powers (NEPs) at or below 0.1 aW/sqrt(Hz) to be subdominant to noise from sky backgrounds and thermal noise from PRIMA's cryogenically cooled primary mirror. Using a Radio Frequency System on a Chip for multitone readout, we measure the NEPs of detectors on a flight-like array designed to observe at a wavelength of 210 microns. We find that 92% of the KIDs measured have an NEP below 0.1 aW/sqrt(Hz) at a noise frequency of 10 Hz.

astro-ph.IM↗

arXiv2024

ICE-SEARCH: A Language Model-Driven Feature Selection Approach

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LLMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.

cs.LG↗

arXiv2024

The Cosmic Ultraviolet Baryon Survey (CUBS) VIII: Group Environment of the Most Luminous Quasars at $z\approx1$

We investigate the group-scale environment of 15 luminous quasars (luminosity $L_{\rm 3000}>10^{46}$ erg s$^{-1}$) from the Cosmic Ultraviolet Baryon Survey (CUBS) at redshift $z\approx1$. Using the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph on the Very Large Telescope (VLT), we conduct a deep galaxy redshift survey in the CUBS quasar fields to identify group members and measure the physical properties of individual galaxies and galaxy groups. We find that the CUBS quasars reside in diverse environments. The majority (11 out of 15) of the CUBS quasars reside in overdense environments with typical halo masses exceeding $10^{13}{\rm M}_{\odot}$, while the remaining quasars reside in moderate-size galaxy groups. No correlation is observed between overdensity and redshift, black hole (BH) mass, or luminosity. Radio-loud quasars (5 out of 15 CUBS quasars) are more likely to be in overdense environments than their radio-quiet counterparts in the sample, consistent with the mean trends from previous statistical observations and clustering analyses. Nonetheless, we also observe radio-loud quasars in moderate groups and radio-quiet quasars in overdense environments, indicating a large scatter in the connection between radio properties and environment. We find that the most UV luminous quasars might be outliers in the stellar mass-to-halo mass relations or may represent departures from the standard single-epoch BH relations.

astro-ph.GA↗

arXiv2024

An ensemble study of turbulence in extended QSO nebulae at $z\approx0.5$--1

Turbulent motions in the circumgalactic medium (CGM) play a critical role in regulating the evolution of galaxies, yet their detailed characterization remains elusive. Using two-dimensional velocity maps constructed from spatially-extended [OII] and [OIII] emission, Chen et al. (2023b) measured the velocity structure functions (VSFs) of four quasar nebulae at $z\approx\!0.5$--1.1. One of these exhibits a spectacular Kolmogorov relation. Here we carry out an ensemble study using an expanded sample incorporating four new nebulae from three additional QSO fields. The VSFs measured for all eight nebulae are best explained by subsonic turbulence revealed by the line-emitting gas, which in turn strongly suggests that the cool gas ($T\!\sim\!10^4$ K) is dynamically coupled to the hot ambient medium. Previous work demonstrates that the largest nebulae in our sample reside in group environments with clear signs of tidal interactions, suggesting that environmental effects are vital in seeding and enhancing turbulence within the gaseous halos, ultimately promoting the formation of the extended nebulae. No discernible differences are observed in the VSF properties between radio-loud and radio-quiet QSO fields. We estimate the turbulent heating rate per unit volume, $Q_{\rm turb}$, in the QSO nebulae to be $\sim 10^{-26}$--$10^{-22}$ erg cm$^{-3}$ s$^{-1}$ for the cool phase and $\sim 10^{-28}$--$10^{-25}$ erg cm$^{-3}$ s$^{-1}$ for the hot phase. This range aligns with measurements in the intracluster medium and star-forming molecular clouds but is $\sim10^3$ times higher than the $Q_{\rm turb}$ observed inside cool gas clumps on scales $\lesssim1$ kpc using absorption-line techniques. We discuss the prospect of bridging the gap between emission and absorption studies by pushing the emission-based VSF measurements to below $\approx\!10$ kpc.

astro-ph.GA↗

arXiv2023

Low Overhead Beam Alignment for Mobile Millimeter Channel Based on Continuous-Time Prediction

In millimeter-wave (mmWave) communications, directional transmission based on beamforming is important to compensate for high pathloss. To maintain the desired direction transmission gain, beam scanning that involves the transmitter sending the pilot signal over all available beam directions to find the optimal beam is often considered. Alternatively, beam tracking using partial beams can save the beam training overhead through algorithms such as statistical analysis models and kalman filter (KF). Unfortunately, existing beam tracking solutions are limited to a fixed beam variation pattern. In this work, we propose an adaptive online beam alignment (AOBA) scheme, which aims to reduce training overhead and achieve accurate beam alignment for any movement profile. The proposed AOBA periodically performs beam tracking using a small amount but carefully selected candidate beams and switches to beam scanning using all available beams based on a given switching rule. During the interval without the pilot signal, the optimal beam at an arbitrary time instant is predicted with the aid of the recently proposed ordinary differential equation (ODE)-long short-term memory (LSTM) model. Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.

eess.SY↗

arXiv2023

Multiply robust estimators in longitudinal studies with missing data under control-based imputation

Longitudinal studies are often subject to missing data. The ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classically envisioned control-based scenario for the treatment effect evaluation using the hypothetical strategy, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed under the assumptions addressed by J2R, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves $n^{1/2}$-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than $n^{-1/4}$ when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.

stat.ME↗

arXiv2023

Modeling of cosmic rays and near-IR photons in aluminum KIDs

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is working to develop kinetic inductance detectors (KIDs) that can meet the sensitivity targets of a far-infrared spectrometer on a cryogenically cooled space telescope. An important ingredient for achieving high sensitivity is increasing the fractional-frequency responsivity. Here we present a study of the responsivity of aluminum KIDs fabricated at the Jet Propulsion Laboratory. Specifically, we model the KID's temporal response to pair-breaking excitations in the framework of the Mattis-Bardeen theory, incorporating quasiparticle recombination dynamics and the pair-breaking efficiency. Using a near-IR laser, we measure time-resolved photon pulses and fit them to our model, extracting the time-resolved quasiparticle density and the quasiparticle recombination lifetime. Comparing the fit to the known energy of the laser provides a measurement of the pair-breaking efficiency. In addition to photon-sourced excitations, it is important to understand the KID's response to phonon-sourced excitations from cosmic rays. We measure the rate of secondary cosmic rays detected by our devices, and predict the dead time due to cosmic rays for an array in L2 orbit. This work provides confidence in KIDs' robustness to cosmic ray events in the space environment.

astro-ph.IM↗

arXiv2023

QoS-Aware Downlink Beamforming for Joint Transmission in Multi-Cell Networks

Multi-cell cooperation is an effective means to improve service quality to cellular users. Existing work primarily focuses on interference cancellation using all the degrees of freedom (DoF). This leads to low service quality for some users with poor channel quality to its serving base station. This work investigates the multi-cell beamforming design for simultaneously enhancing the downlink signal strength and mitigating interference. We first consider the ideal case when perfect channel state information (CSI) is available for determining the beamforming vectors and then extend to the case of imperfect CSI. For both cases, the beamforming optimization problems are non-convex. Assuming perfect CSI, we obtain the optimal joint transmit (JT) beamforming vectors based on the uplink-downlink duality. In the presence of unknown CSI errors, we use the semidefinite relaxation (SDR) with Bernstein-type inequality to derive the robust JT beamforming. Numerical results are presented to evaluate the performance of the proposed schemes.

cs.IT↗

arXiv2023

Average AoI Minimization for Energy Harvesting Relay-aided Status Update Network Using Deep Reinforcement Learning

A dual-hop status update system aided by energy harvesting (EH) relays with finite data and energy buffers is studied in this work. To achieve timely status updates, the best relays should be selected to minimize the average age of information (AoI), which is a recently proposed metric to evaluate information freshness. The average AoI minimization can be formulated as a Markov decision process (MDP), but the state space for capturing channel and buffer evolution grows exponentially with the number of relays, leading to high solution complexity. We propose a relay selection (RS) scheme based on deep reinforcement learning (DRL) according to the instantaneous channel packet freshness and buffer information of each relay. Simulation results show a significant improvement of the proposed DRL-based RS scheme over state-of-art approaches.

eess.SY↗

arXiv2023

On the Channel Correlation in Reconfigurable Intelligent Surface-Aided System

This works explores the correlation between channels in reconfigurable intelligent surface (RIS)-aided communication systems. In this type of system, an RIS made up of many passive elements with adjustable phases reflects the transmitter's signal to the receiver. Since the transmitter-RIS link may be shared by multiple receivers, the cascade channels of two receivers may experience correlated fading, which can negatively impact system performance. Using the mean correlation coefficient as a metric, we analyze the correlation between two cascade channels and derive an accurate approximation in closed form. We also consider the extreme case of an infinitely large number of RIS elements and obtain a convergence result. Our analysis accuracy is validated by simulation results, which offer insights into the correlation characteristics of RIS-aided fading channels.

cs.IT↗

arXiv2022

Directly tracing cool filamentary accretion over >100 kpc into the interstellar medium of a quasar host at z=1

We report the discovery of giant (50-100 kpc) [O II] emitting nebulae with the Multi-Unit Spectroscopic Explorer (MUSE) in the field of TXS 0206-048, a luminous quasar at z=1.13. Down-the-barrel UV spectra of the quasar show absorption at velocities coincident with those of the extended nebulae, enabling new insights into inflows and outflows around the quasar host. One nebula exhibits a filamentary morphology extending over 120 kpc from the halo toward the quasar and intersecting with another nebula surrounding the quasar host with a radius of 50 kpc. This is the longest cool filament observed to-date and arises at higher redshift and in a less massive system than those in cool-core clusters. The filamentary nebula has line-of-sight velocities >300 km/s from nearby galaxies but matches that of the nebula surrounding the quasar host where they intersect, consistent with accretion of cool inter- or circum-galactic medium or cooling hot halo gas. The kinematics of the nebulae surrounding the quasar host are unusual and complex, with redshifted and blueshifted spiral-like structures. The emission velocities at 5-10 kpc from the quasar match those of inflowing absorbing gas observed in UV spectra of the quasar. Together, the extended nebulae and associated redshifted absorption represent a compelling case of cool, filamentary gas accretion from halo scales into the extended interstellar medium and toward the nucleus of a massive quasar host. The inflow rate implied by the combined emission and absorption constraints is well below levels required to sustain the quasar's radiative luminosity, suggesting anisotropic or variable accretion.

astro-ph.GA↗

arXiv2022

Anisotropic magnetic properties and tunable conductivity in two-dimensional layered NaCrX2 (X=Te,Se,S) single crystals

Monolayer NaCrX2 (X=Te,Se,S) were theoretically proposed to be two-dimensional intrinsic ferromagnetic semiconductors while their physical properties have not been thoroughly investigated in bulk single crystals. We report the single-crystal growth, structural, magnetic and electronic transport properties of NaCr(Te1-xSex)2 (0 6 x 6 1) and NaCrS2. For NaCr(Te1-xSex)2, the strong perpendicular magnetic anisotropy of NaCrTe2 can be gradually tuned to be a nearly isotropic one by Se-doping. Meanwhile, a systematic change in the conductivity with increasing x is observed, displaying a doping-induced metal-insulator-like transition. Under magnetic field larger than 30 koe, both NaCrTe2 and NaCrSe2 can be polarized to a ferromagnetic state. While for NaCrS2, robust antiferromagnetism is observed up to 70 kOe and two field-induced metamagnetic transitions are identified along H||ab. These intriguing properties together with the potential to be exfoliated down to few-layer thickness make NaCrX2 (X=Te,Se,S) promising for exploring spintronic applications.

cond-mat.mtrl-sci↗

arXiv2022

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a general robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in both the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation, in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.

stat.ME↗

arXiv2022

Sensitivity analysis in longitudinal clinical trials via distributional imputation

Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analysis is critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies often request using imputation models such as return-to-baseline, control-based, and washout imputation. Multiple imputation is popular in sensitivity analysis; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin's combining rule. We propose distributional imputation (DI) in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the DI estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The finite-sample performance of DI inference is assessed in the simulation study. We apply the proposed framework to an antidepressant longitudinal clinical trial involving missing data to investigate the robustness of the treatment effect. Our proposed DI approach detects a statistically significant treatment effect in both the primary analysis and sensitivity analysis under certain prespecified sensitivity models in terms of the average treatment effect, the risk difference, and the quantile treatment effect in lower quantiles of the responses, uncovering the benefit of the test drug for curing depression.

stat.ME↗

arXiv2022

Research on Parallel SVM Algorithm Based on Cascade SVM

Cascade SVM (CSVM) can group datasets and train subsets in parallel, which greatly reduces the training time and memory consumption. However, the model accuracy obtained by using this method has some errors compared with direct training. In order to reduce the error, we analyze the causes of error in grouping training, and summarize the grouping without error under ideal conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which balances the sample proportion in the subset after grouping to ensure that the sample proportion in the subset is the same as the original dataset. At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM. Finally, two common datasets are used for experimental verification, and the results show that the accuracy error obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is reduced by an order of magnitude.

cs.LG↗

arXiv2022

Kubric: A scalable dataset generator

Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.

cs.CV↗