131 research outputs found
Prompt2NeRF-PIL: Fast NeRF Generation via Pretrained Implicit Latent
This paper explores promptable NeRF generation (e.g., text prompt or single
image prompt) for direct conditioning and fast generation of NeRF parameters
for the underlying 3D scenes, thus undoing complex intermediate steps while
providing full 3D generation with conditional control. Unlike previous
diffusion-CLIP-based pipelines that involve tedious per-prompt optimizations,
Prompt2NeRF-PIL is capable of generating a variety of 3D objects with a single
forward pass, leveraging a pre-trained implicit latent space of NeRF
parameters. Furthermore, in zero-shot tasks, our experiments demonstrate that
the NeRFs produced by our method serve as semantically informative
initializations, significantly accelerating the inference process of existing
prompt-to-NeRF methods. Specifically, we will show that our approach speeds up
the text-to-NeRF model DreamFusion and the 3D reconstruction speed of the
image-to-NeRF method Zero-1-to-3 by 3 to 5 times
Stable mode-locked pulses from mid-infrared semiconductor lasers
We report the unequivocal demonstration of mid-infrared mode-locked pulses
from a semiconductor laser. The train of short pulses was generated by actively
modulating the current and hence the optical gain in a small section of an
edge-emitting quantum cascade laser (QCL). Pulses with pulse duration at
full-width-at-half-maximum of about 3 ps and energy of 0.5 pJ were
characterized using a second-order interferometric autocorrelation technique
based on a nonlinear quantum well infrared photodetector. The mode-locking
dynamics in the QCLs was modelled and simulated based on Maxwell-Bloch
equations in an open two-level system. We anticipate our results to be a
significant step toward a compact, electrically-pumped source generating
ultrashort light pulses in the mid-infrared and terahertz spectral ranges.Comment: 26 pages, 4 figure
Mitigating the Alignment Tax of RLHF
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs
under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting,
which is also known as the alignment tax. To empirically verify this
hypothesis, we conducted experiments with existing RLHF algorithms using
OpenLLaMA-3B, which revealed a pronounced alignment tax in NLP tasks. On the
other hand, despite various techniques to mitigate forgetting, they are often
at odds with the RLHF performance, leading to a trade-off between reward
maximization and forgetting mitigation.
In light of the above pressing issue in aligning LLMs, in this paper we
explore model averaging, which interpolates between pre and post RLHF model
weights, to achieve a more efficient reward-tax Pareto front. To understand its
effectiveness, We offer theoretical insights into model averaging, revealing
that it enhances performance Pareto front by increasing feature diversity on
the layers where tasks share overlapped feature spaces. Empirical evidence
corroborates our analysis by showing the benefits of averaging low-level
transformer layers. Building on the analysis and the observation that averaging
different layers of the transformer leads to significantly different reward-tax
trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find
various combination ratios of model layers. AMA seeks to maximize the alignment
reward while incurring minimal alignment tax. Moreover, we validate AMA's
performance across a range of RLHF algorithms over OpenLLaMA-3B and further
extend our findings to Mistral-7B.Comment: 28 Page
Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics
The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino
detector currently under construction in China. Thanks to the tight
requirements on its optical and radio-purity properties, it will be able to
perform leading measurements detecting terrestrial and astrophysical neutrinos
in a wide energy range from tens of keV to hundreds of MeV. A key requirement
for the success of the experiment is an unprecedented 3% energy resolution,
guaranteed by its large active mass (20 kton) and the use of more than 20,000
20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution
sampling electronics located very close to the PMTs. As the Front-End and
Read-Out electronics is expected to continuously run underwater for 30 years, a
reliable readout acquisition system capable of handling the timestamped data
stream coming from the Large-PMTs and permitting to simultaneously monitor and
operate remotely the inaccessible electronics had to be developed. In this
contribution, the firmware and hardware implementation of the IPbus based
readout protocol will be presented, together with the performances measured on
final modules during the mass production of the electronics
Mass testing of the JUNO experiment 20-inch PMTs readout electronics
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose,
large size, liquid scintillator experiment under construction in China. JUNO
will perform leading measurements detecting neutrinos from different sources
(reactor, terrestrial and astrophysical neutrinos) covering a wide energy range
(from 200 keV to several GeV). This paper focuses on the design and development
of a test protocol for the 20-inch PMT underwater readout electronics,
performed in parallel to the mass production line. In a time period of about
ten months, a total number of 6950 electronic boards were tested with an
acceptance yield of 99.1%
Validation and integration tests of the JUNO 20-inch PMTs readout electronics
The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino
detector currently under construction in China. JUNO will be able to study the
neutrino mass ordering and to perform leading measurements detecting
terrestrial and astrophysical neutrinos in a wide energy range, spanning from
200 keV to several GeV. Given the ambitious physics goals of JUNO, the
electronic system has to meet specific tight requirements, and a thorough
characterization is required. The present paper describes the tests performed
on the readout modules to measure their performances.Comment: 20 pages, 13 figure
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
- …