arXiv subjects

Shuhao Liu

Publications and source records attributed to Shuhao Liu.

6 recordsLinked to original sources

arXiv2025

Efficient Training for Human Video Generation with Entropy-Guided Prioritized Progressive Learning

Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training these models on high-resolution, multi-frame data pose significant challenges. In this paper, we propose Entropy-Guided Prioritized Progressive Learning (Ent-Prog), an efficient training framework tailored for diffusion models on human video generation. First, we introduce Conditional Entropy Inflation (CEI) to assess the importance of different model components on the target conditional generation task, enabling prioritized training of the most critical components. Second, we introduce an adaptive progressive schedule that adaptively increases computational complexity during training by measuring the convergence efficiency. Ent-Prog reduces both training time and GPU memory consumption while maintaining model performance. Extensive experiments across three datasets, demonstrate the effectiveness of Ent-Prog, achieving up to 2.2$\times$ training speedup and 2.4$\times$ GPU memory reduction without compromising generative performance.

cs.CV↗

arXiv2021

Interaction Effects and Viscous Magneto-Transport in a Strongly Correlated 2D Hole System

Fermi liquid theory has been a foundation in understanding the electronic properties of materials. For weakly interacting two-dimensional (2D) electron or hole systems, electron-electron interactions are known to introduce quantum corrections to the Drude conductivity in the FL theory, giving rise to temperature dependent conductivity and magneto-resistance. Here we study the magneto-transport in a strongly interacting 2D hole system over a broad range of temperatures ($T$ = 0.09 to $>$1K) and densities $p=1.98-0.99\times10^{10}$ cm$^{-2}$ where the ratio between Coulomb energy and Fermi energy $r_s$ = 20 - 30. We show that while the system exhibits a negative parabolic magneto-resistance at low temperatures ($\lesssim$ 0.4K) characteristic of an interacting FL, the FL interaction corrections represent an insignificant fraction of the total conductivity. Surprisingly, a positive magneto-resistance emerges at high temperatures and grows with increasing temperature even in the regime $T \sim E_F$, close to the Fermi temperature. This unusual positive magneto-resistance at high temperatures is attributed to the collective viscous transport of 2D hole fluid in the hydrodynamic regime where holes scatter frequently with each other. These findings highlight the collective transport in a strongly interacting 2D system in the $r_s\gg 1$ regime and the hydrodynamic transport induced magneto-resistance opens up possibilities to new routes of magneto-resistance at high temperatures.

cond-mat.str-el↗

arXiv2018

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art.

cs.IR↗

arXiv2016

Imaging the Long Transport Lengths of Photo-generated Carriers in Oriented Perovskite Films

Organometal halide perovskite has emerged as a promising material for solar cells and optoelectronics. Although the long diffusion length of photo-generated carriers is believed to be a critical factor responsible for the material's high efficiency in solar cells, a direct study of carrier transport over long distances in organometal halide perovskites is still lacking. We fabricated highly oriented crystalline CH$_3$NH$_3$PbI$_3$ (MAPbI$_3$) thin film lateral transport devices with long channel length (~ 120 $μ$m). By performing spatially scanned photocurrent imaging measurements with local illumination, we directly show that the perovskite films prepared here have very long transport lengths for photo-generated carriers, with a minority carrier (electron) diffusion length on the order of 10 $μ$m. Our approach of applying scanning photocurrent microscopy to organometal halide perovskites may be further used to elucidate the carrier transport processes and vastly different carrier diffusion lengths (~ 100 nm to 100 $μ$m) in different types of organometal halide perovskites.

cond-mat.mtrl-sci↗

arXiv2014

Low Latency Datacenter Networking: A Short Survey

Datacenters are the cornerstone of the big data infrastructure supporting numerous online services. The demand for interactivity, which significantly impacts user experience and provider revenue, is translated into stringent timing requirements for flows in datacenter networks. Thus low latency networking is becoming a major concern of both industry and academia. We provide a short survey of recent progress made by the networking community for low latency datacenter networks. We propose a taxonomy to categorize existing work based on four main techniques, reducing queue length, accelerating retransmissions, prioritizing mice flows, and exploiting multi-path. Then we review select papers, highlight the principal ideas, and discuss their pros and cons. We also present our perspectives of the research challenges and opportunities, hoping to aspire more future work in this space.

cs.NI↗

arXiv2014

RepNet: Cutting Tail Latency in Data Center Networks with Flow Replication

Data center networks need to provide low latency, especially at the tail, as demanded by many interactive applications. To improve tail latency, existing approaches require modifications to switch hardware and/or end-host operating systems, making them difficult to be deployed. We present the design, implementation, and evaluation of RepNet, an application layer transport that can be deployed today. RepNet exploits the fact that only a few paths among many are congested at any moment in the network, and applies simple flow replication to mice flows to opportunistically use the less congested path. RepNet has two designs for flow replication: (1) RepSYN, which only replicates SYN packets and uses the first connection that finishes TCP handshaking for data transmission, and (2) RepFlow which replicates the entire mice flow. We implement RepNet on {\tt node.js}, one of the most commonly used platforms for networked interactive applications. {\tt node}'s single threaded event-loop and non-blocking I/O make flow replication highly efficient. Performance evaluation on a real network testbed and in Mininet reveals that RepNet is able to reduce the tail latency of mice flows, as well as application completion times, by more than 50\%.

cs.NI↗