220 research outputs found
Non-simple systoles on random hyperbolic surfaces for large genus
In this paper, we investigate the asymptotic behavior of the non-simple
systole, which is the length of a shortest non-simple closed geodesic, on a
random closed hyperbolic surface on the moduli space of Riemann
surfaces of genus endowed with the Weil-Petersson measure. We show that as
the genus goes to infinity, the non-simple systole of a generic hyperbolic
surface in behaves exactly like .Comment: 47 pages, 11 figures. Comments welcom
Two dimensional vertex-decorated Lieb lattice with exact mobility edges and robust flat bands
The mobility edge (ME) that marks the energy separating extended and
localized states is a central concept in understanding the metal-insulator
transition induced by disordered or quasiperiodic potentials. MEs have been
extensively studied in three dimensional disorder systems and one-dimensional
quasiperiodic systems. However, the studies of MEs in two dimensional (2D)
systems are rare. Here we propose a class of 2D vertex-decorated Lieb lattice
models with quasiperiodic potentials only acting on the vertices of a
(extended) Lieb lattice. By mapping these models to the 2D Aubry-Andr\'{e}
model, we obtain exact expressions of MEs and the localization lengths of
localized states, and further demonstrate that the flat bands remain unaffected
by the quasiperiodic potentials. Finally, we propose a highly feasible scheme
to experimentally realize our model in a quantum dot array. Our results open
the door to studying and realizing exact MEs and robust flat bands in 2D
systems
Color-Perception-Guided Display Power Reduction for Virtual Reality
Battery life is an increasingly urgent challenge for today's untethered VR
and AR devices. However, the power efficiency of head-mounted displays is
naturally at odds with growing computational requirements driven by better
resolution, refresh rate, and dynamic ranges, all of which reduce the sustained
usage time of untethered AR/VR devices. For instance, the Oculus Quest 2, under
a fully-charged battery, can sustain only 2 to 3 hours of operation time. Prior
display power reduction techniques mostly target smartphone displays. Directly
applying smartphone display power reduction techniques, however, degrades the
visual perception in AR/VR with noticeable artifacts. For instance, the
"power-saving mode" on smartphones uniformly lowers the pixel luminance across
the display and, as a result, presents an overall darkened visual perception to
users if directly applied to VR content.
Our key insight is that VR display power reduction must be cognizant of the
gaze-contingent nature of high field-of-view VR displays. To that end, we
present a gaze-contingent system that, without degrading luminance, minimizes
the display power consumption while preserving high visual fidelity when users
actively view immersive video sequences. This is enabled by constructing a
gaze-contingent color discrimination model through psychophysical studies, and
a display power model (with respect to pixel color) through real-device
measurements. Critically, due to the careful design decisions made in
constructing the two models, our algorithm is cast as a constrained
optimization problem with a closed-form solution, which can be implemented as a
real-time, image-space shader. We evaluate our system using a series of
psychophysical studies and large-scale analyses on natural images. Experiment
results show that our system reduces the display power by as much as 24% with
little to no perceptual fidelity degradation
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive
capabilities in a variety of natural language processing (NLP) tasks.
Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge
devices with limited computing, memory, and energy budgets. In this paper, we
propose Confidant, a multi-backend collaborative training framework for
customizing state-of-the-art LLMs on commodity mobile devices like smartphones.
Confidant partitions an LLM into several sub-models so that each fits into a
mobile device's memory. A pipeline parallel training mechanism is further
developed to ensure fast and efficient distributed training. In addition, we
propose a novel backend scheduler to allocate different attention heads to
heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the
compute resource utilization on each edge device. Our preliminary experimental
results show that Confidant achieves at most 45.3% memory reduction and 8.03x
inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202
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