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Liu

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ResearcherUnclaimed source record
Information Theorymath.ITMachine Learningastro-ph.SRComputer Visionhep-phMethodologyastro-ph.GAcond-mat.mtrl-scieess.SYGraphicsHuman-Computer Interactionnucl-thSystems and Control

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Published work

22 published item(s)

preprint2026arXiv

Enhancing Consistency Models for Multi-Agent Trajectory Prediction

Diffusion models for multi-agent trajectory prediction are limited by iterative denoising, which causes inference latency that hinders their use in time-critical settings like autonomous driving. Fast-sampling variants using DDIM and informed initial noise distributions partially alleviate this issue, but they either fail to achieve true single-step generation or are constrained by the chosen noise distribution. Consistency Models (CMs) offer high-quality one-step generation by mapping noise directly to data, but are difficult to train from scratch . We propose ECTraj, an enhanced CM pipeline with improved training and conditional generation for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard outputs, while the teacher explicitly fuses its predictions with parts of the ground truth to give stronger supervision. We also exploit CMs' direct denoising for top-K multi-shot generation during training. Combining conditional generation with this enhanced consistency objective yields faster inference and improved prediction accuracy, establishing competitive new benchmarks on the large-scale Argoverse 2 dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Understanding the Role of Context in Creating Enjoyable Co-Located Interactions

In recent years, public discourse has blamed digital technologies for making people feel "alone together," distracting us from engaging with one another, even when we are interacting in-person. We argue that in order to design technologies that foster and augment co-located interactions, we need to first understand the context in which enjoyable co-located socialization takes place. We address this gap by surveying and interviewing over 1,000 U.S.-based participants to understand what, where, with whom, how, and why people enjoy spending time in-person. Our findings suggest that people enjoy engaging in everyday activities with individuals with whom they have strong social ties because it helps enable nonverbal cues, facilitate spontaneity, support authenticity, encourage undivided attention, and leverage the physicality of their bodies and the environment. We conclude by providing a set of recommendations for designers interested in creating co-located technologies that encourage social engagement and relationship building.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem

We investigate the sparse linear contextual bandit problem where the parameter $θ$ is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the context is generated adversarilly but with small random non-adaptive perturbations. We prove that the simple online Lasso supports sparse linear contextual bandit with regret bound $\mathcal{O}(\sqrt{kT\log d})$ even when $d \gg T$ where $k$ and $d$ are the number of effective and ambient dimension, respectively. Compared to the recent work from Sivakumar et al. (2020), our analysis does not rely on the precondition processing, adaptive perturbation (the adaptive perturbation violates the i.i.d perturbation setting) or truncation on the error set. Moreover, the special structures in our results explicitly characterize how the perturbation affects exploration length, guide the design of perturbation together with the fundamental performance limit of perturbation method. Numerical experiments are provided to complement the theoretical analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Dual Link Algorithm for the Weighted Sum Rate Maximization in MIMO Interference Channels

MIMO interference network optimization is important for increasingly crowded wireless communication networks. We provide a new algorithm, named Dual Link algorithm, for the classic problem of weighted sum-rate maximization for MIMO multiaccess channels (MAC), broadcast channels (BC), and general MIMO interference channels with Gaussian input and a total power constraint. For MIMO MAC/BC, the algorithm finds optimal signals to achieve the capacity region boundary. For interference channels with Gaussian input assumption, two of the previous state-of-the-art algorithms are the WMMSE algorithm and the polite water-filling (PWF) algorithm. The WMMSE algorithm is provably convergent, while the PWF algorithm takes the advantage of the optimal transmit signal structure and converges the fastest in most situations but is not guaranteed to converge in all situations. It is highly desirable to design an algorithm that has the advantages of both algorithms. The dual link algorithm is such an algorithm. Its fast and guaranteed convergence is important to distributed implementation and time varying channels. In addition, the technique and a scaling invariance property used in the convergence proof may find applications in other non-convex problems in communication networks.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Rotor-phonon coupling in perovskite CH3NH3PbI3: the origin of exceptional transport properties

Atomic dynamics takes a fundamental part in numbers of physical properties of solids like high-Tc superconductivity, semiconducting transports, and thermoelectricity. Perovskite CH3NH3PbI3 exhibits outstanding photovoltaic performances, but the exact physical scenario has not been established yet, due to the inadequate understanding of the atomic dynamics and exceptional transport properties. We present a complete atomic dynamic picture consisting of phonons, rotational modes of protons and molecular vibrational modes, which is constructed by carrying out high-resolution time-of-flight inelastic neutron scattering measurements in a wide energy window ranging from 0.0036 to 54 meV on a large single crystal sample. A three-fold rotational mode of protons activated around 80 K reduces the lifetimes of acoustic and optical phonons down to about 4.5 ps and below 1 ps at 150 K, respectively. The orthorhombic to tetragonal phase transition takes place with a slower four-fold rotational mode of the C-N axis concomitantly setting in at ~ 165 K, above which the optical phonons are too broadened to be distinguished whereas the acoustic ones are still robust. The significantly reduced lifetimes of optical phonons are linked to the smaller mobility of charge carriers while the ultralow lattice thermal conductivity is attributed to nanoscale mean free paths of acoustic phonons. These microscopic insights provide a solid standing point, on which perovskite solar cells can be understood more accurately and their performances are perhaps further optimized. The revealed rotor-phonon coupling opens up an emergent opportunity to create unprecedented functionalities of materials.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Downlink Analysis for a Heterogeneous Cellular Network

In this paper, a comprehensive study of the the downlink performance in a heterogeneous cellular network (or hetnet) is conducted. A general hetnet model is considered consisting of an arbitrary number of open-access and closed-access tier of base stations (BSs) arranged according to independent homogeneous Poisson point processes. The BSs of each tier have a constant transmission power, random fading coefficient with an arbitrary distribution and arbitrary path-loss exponent of the power-law path-loss model. For such a system, analytical characterizations for the coverage probability and average rate at an arbitrary mobile-station (MS), and average per-tier load are derived for both the max-SINR connectivity and nearest-BS connectivity models. Using stochastic ordering, interesting properties and simplifications for the hetnet downlink performance are derived by relating these two connectivity models to the maximum instantaneous received power (MIRP) connectivity model and the maximum biased received power (MBRP) connectivity models, respectively, providing good insights about the hetnets and the downlink performance in these complex networks. Furthermore, the results also demonstrate the effectiveness and analytical tractability of the stochastic geometric approach to study the hetnet performance.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Duality, Polite Water-filling, and Optimization for MIMO B-MAC Interference Networks and iTree Networks

This paper gives the long sought network version of water-filling named as polite water-filling. Unlike in single-user MIMO channels, where no one uses general purpose optimization algorithms in place of the simple and optimal water-filling for transmitter optimization, the traditional water-filling is generally far from optimal in networks as simple as MIMO multiaccess channels (MAC) and broadcast channels (BC), where steepest ascent algorithms have been used except for the sum-rate optimization. This is changed by the polite water-filling that is optimal for all boundary points of the capacity regions of MAC and BC and for all boundary points of a set of achievable regions of a more general class of MIMO B-MAC interference networks, which is a combination of multiple interfering broadcast channels, from the transmitter point of view, and multiaccess channels, from the receiver point of view, including MAC, BC, interference channels, X networks, and most practical wireless networks as special case. It is polite because it strikes an optimal balance between reducing interference to others and maximizing a link's own rate. Employing it, the related optimizations can be vastly simplified by taking advantage of the structure of the problems. Deeply connected to the polite water-filling, the rate duality is extended to the forward and reverse links of the B-MAC networks. As a demonstration, weighted sum-rate maximization algorithms based on polite water-filling and duality with superior performance and low complexity are designed for B-MAC networks and are analyzed for Interference Tree (iTree) Networks, a sub-class of the B-MAC networks that possesses promising properties for further information theoretic study.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Magnetic Fields and Massive Star Formation

Massive stars ($M > 8$ \msun) typically form in parsec-scale molecular clumps that collapse and fragment, leading to the birth of a cluster of stellar objects. We investigate the role of magnetic fields in this process through dust polarization at 870 $μ$m obtained with the Submillimeter Array (SMA). The SMA observations reveal polarization at scales of $\lsim$ 0.1 pc. The polarization pattern in these objects ranges from ordered hour-glass configurations to more chaotic distributions. By comparing the SMA data with the single dish data at parsec scales, we found that magnetic fields at dense core scales are either aligned within $40^\circ$ of or perpendicular to the parsec-scale magnetic fields. This finding indicates that magnetic fields play an important role during the collapse and fragmentation of massive molecular clumps and the formation of dense cores. We further compare magnetic fields in dense cores with the major axis of molecular outflows. Despite a limited number of outflows, we found that the outflow axis appears to be randomly oriented with respect to the magnetic field in the core. This result suggests that at the scale of accretion disks ($\lsim 10^3$ AU), angular momentum and dynamic interactions possibly due to close binary or multiple systems dominate over magnetic fields. With this unprecedentedly large sample massive clumps, we argue on a statistical basis that magnetic fields play an important role during the formation of dense cores at spatial scale of 0.01 - 0.1 pc in the context of massive star and cluster star formation.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Solar Flares with an Exponential Growth of the Emission Measure in the Impulsive Phase Derived from X-ray Observations

The light curves of solar flares in the impulsive phase are complex in general, indicating that multiple physical processes are involved in. With the GOES (Geostationary Operational Environmental Satellite) observations, we find that there are a subset of flares, whose impulsive phases are dominated by a period of exponential growth of the emission measure. The flares occurred from January 1999 to December 2002 are analyzed, and the results from the observations made with both GOES 8 and GEOS 10 satellites are compared to estimate the instrumental uncertainties. Their mean temperatures during this exponential growth phase have a normal distribution. Most flares within the 1σ range of this temperature distribution belong to the GOES class B or C, with the peak fluxes at the GOES low-energy channel following a log-normal distribution. The growth rate and duration of the exponential growth phase also follow a lognormal distribution, in which the duration is distributed in the range from half a minute to about half an hour. As expected, the growth time is correlated with the decay time of the soft X-ray flux. We also find that the growth rate of the emission measure is strongly anti-correlated with the duration of the exponential growth phase, and the mean temperature increases slightly with the increase of the growth rate. The implications of these results on the study of energy release in solar flares are discussed in the end.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

UV and EUV Emissions at the Flare Foot-points Observed by AIA

A solar flare is composed of impulsive energy release events by magnetic reconnection, which forms and heats flare loops. Recent studies have revealed a two-phase evolution pattern of UV 1600Å emission at the feet of these loops: a rapid pulse lasting for a few seconds to a few minutes, followed by a gradual decay on timescales of a few tens of minutes. Multiple band EUV observations by AIA further reveal very similar signatures. These two phases represent different but related signatures of an impulsive energy release in the corona. The rapid pulse is an immediate response of the lower atmosphere to an intense thermal conduction flux resulting from the sudden heating of the corona to high temperatures (we rule out energetic particles due to a lack of significant hard X-ray emission). The gradual phase is associated with the cooling of hot plasma that has been evaporated into the corona. The observed footpoint emission is again powered by thermal conduction (and enthalpy), but now during a period when approximate steady state conditions are established in the loop. UV and EUV light curves of individual pixels may therefore be separated into contributions from two distinct physical mechanisms to shed light on the nature of energy transport in a flare. We demonstrate this technique using coordinated, spatially resolved observations of UV and EUV emission from the footpoints of a C3.2 thermal flare.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Downlink Coverage Analysis in a Heterogeneous Cellular Network

In this paper, we consider the downlink signal-to-interference-plus-noise ratio (SINR) analysis in a heterogeneous cellular network with K tiers. Each tier is characterized by a base-station (BS) arrangement according to a homogeneous Poisson point process with certain BS density, transmission power, random shadow fading factors with arbitrary distribution, arbitrary path-loss exponent and a certain bias towards admitting the mobile-station (MS). The MS associates with the BS that has the maximum SINR under the open access cell association scheme. For such a general setting, we provide an analytical characterization of the coverage probability at the MS.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Multi-tier Network Performance Analysis using a Shotgun Cellular System

This paper studies the carrier-to-interference ratio (CIR) and carrier-to-interference-plus-noise ratio (CINR) performance at the mobile station (MS) within a multi-tier network composed of M tiers of wireless networks, with each tier modeled as the homogeneous n-dimensional (n-D, n=1,2, and 3) shotgun cellular system, where the base station (BS) distribution is given by the homogeneous Poisson point process in n-D. The CIR and CINR at the MS in a single tier network are thoroughly analyzed to simplify the analysis of the multi-tier network. For the multi-tier network with given system parameters, the following are the main results of this paper: (1) semi-analytical expressions for the tail probabilities of CIR and CINR; (2) a closed form expression for the tail probability of CIR in the range [1,Infinity); (3) a closed form expression for the tail probability of an approximation to CIR in the entire range [0,Infinity); (4) a lookup table based approach for obtaining the tail probability of CINR, and (5) the study of the effect of shadow fading and BSs with ideal sectorized antennas on the CIR and CINR. Based on these results, it is shown that, in a practical cellular system, the installation of additional wireless networks (microcells, picocells and femtocells) with low power BSs over the already existing macrocell network will always improve the CINR performance at the MS.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Stochastic Ordering based Carrier-to-Interference Ratio Analysis for the Shotgun Cellular Systems

A simple analytical tool based on stochastic ordering is developed to compare the distributions of carrier-to-interference ratio at the mobile station of two cellular systems where the base stations are distributed randomly according to certain non-homogeneous Poisson point processes. The comparison is conveniently done by studying only the base station densities without having to solve for the distributions of the carrier-to-interference ratio, that are often hard to obtain.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Interference and nuclear medium effects on the eta-mesic nuclear spectrum

The missing-mass spectrum obtained in a recoil-free transfer reaction p($^{27}$Al,$^{3}$He)$π^{-}$p'X is analyzed. We find that the observed peak structure arises from the coherent contributions from two reaction processes in the energy region corresponding to a bound eta ($η$) meson. In one of the processes the intermediate $η$ is captured by the nucleus to form the $η$-mesic nucleus $^{25}$Mg$_η$. In the other process, the $η$ does not form $η$-nucleus bound state. The interference between these two processes has caused the peak of the spectrum to appear at an $η$ binding energy stronger than the actual one. Our analysis also indicates that the data are consistent with an attractive N*(1535)-nucleus interaction at energies below the $η$N threshold.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

MIMO B-MAC Interference Network Optimization under Rate Constraints by Polite Water-filling and Duality

We take two new approaches to design efficient algorithms for transmitter optimization under rate constraints, to guarantee the Quality of Service in general MIMO interference networks, which is a combination of multiple interfering broadcast channels (BC) and multiaccess channels (MAC) and is named B-MAC Networks. Two related optimization problems, maximizing the minimum of weighted rates under a sum-power constraint and minimizing the sum-power under rate constraints, are considered. The first approach takes advantage of existing efficient algorithms for SINR problems by building a bridge between rate and SINR through the design of optimal mappings between them. The approach can be applied to other optimization problems as well. The second approach employs polite water-filling, which is the optimal network version of water-filling that we recently found. It replaces most generic optimization algorithms currently used for networks and reduces the complexity while demonstrating superior performance even in non-convex cases. Both centralized and distributed algorithms are designed and the performance is analyzed in addition to numeric examples.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Technical Report: MIMO B-MAC Interference Network Optimization under Rate Constraints by Polite Water-filling and Duality

We take two new approaches to design efficient algorithms for transmitter optimization under rate constraints to guarantee the Quality of Service in general MIMO interference networks, named B-MAC Networks, which is a combination of multiple interfering broadcast channels (BC) and multiaccess channels (MAC). Two related optimization problems, maximizing the minimum of weighted rates under a sum-power constraint and minimizing the sum-power under rate constraints, are considered. The first approach takes advantage of existing efficient algorithms for SINR problems by building a bridge between rate and SINR through the design of optimal mappings between them so that the problems can be converted to SINR constraint problems. The approach can be applied to other optimization problems as well. The second approach employs polite water-filling, which is the optimal network version of water-filling that we recently found. It replaces almost all generic optimization algorithms currently used for networks and reduces the complexity while demonstrating superior performance even in non-convex cases. Both centralized and distributed algorithms are designed and the performance is analyzed in addition to numeric examples.

0 citations0 reviewsNo code linkedOpen access
preprint1995arXiv

An Overview of $D^0\bar{D}^0$ Mixing Search Techniques: Current Status and Future Prospects

The search for $D^0\bar{D}^0$ mixing may carry a large discovery potential for new physics. This paper discusses the techniques, current experimental status, and future prospects for the mixing search. Some new ideas, applicable to future mixing searches, are introduced. In this paper, the importance of separately measuring the decay rate difference and the mass difference of the two CP eigenstates (in order to observe New Physics) has been emphasized, since the theoretical calculations for long distance effects are still plagued by large uncertainties.

0 citations0 reviewsNo code linkedOpen access