3,351 research outputs found
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Scene representation networks (SRNs) have been recently proposed for
compression and visualization of scientific data. However, state-of-the-art
SRNs do not adapt the allocation of available network parameters to the complex
features found in scientific data, leading to a loss in reconstruction quality.
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN)
and propose a domain decomposition training and inference technique for
accelerated parallel training on multi-GPU systems. We also release an
open-source neural volume rendering application that allows plug-and-play
rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses
multiple spatially adaptive feature grids that learn where to be placed within
the domain to dynamically allocate more neural network resources where error is
high in the volume, improving state-of-the-art reconstruction accuracy of SRNs
for scientific data without requiring expensive octree refining, pruning, and
traversal like previous adaptive models. In our domain decomposition approach
for representing large-scale data, we train an set of APMGSRNs in parallel on
separate bricks of the volume to reduce training time while avoiding overhead
necessary for an out-of-core solution for volumes too large to fit in GPU
memory. After training, the lightweight SRNs are used for realtime neural
volume rendering in our open-source renderer, where arbitrary view angles and
transfer functions can be explored. A copy of this paper, all code, all models
used in our experiments, and all supplemental materials and videos are
available at https://github.com/skywolf829/APMGSRN.Comment: Accepted to IEEE VIS 202
Adversarial Examples with Unlimited Amount of Additions
Malware detection methods based on gray images and deep learning have the characteristics of high detection accuracy and no need of feature engineering. Unfortunately, adversarial examples (AEs) can deceive such detection methods. However, it is difficult to reduce the detection accuracy of this kind of detection method greatly without destroying the functional integrity of the original file. By analyzing the structure and loading mechanism of portable executable (PE) files, this paper proposes an unrestricted add-amount bytecode attack (BAUAA). BAUAA generates adversarial samples by adding bytecode to a “section additional space” in the PE file that is scattered after each section and is not loaded into memory, and because of the unlimited amount of this space that can be added, the generated adversarial samples can be transformed into grayscale images that vary in size and texture, which can affect the discrimination accuracy of gray images and deep learning-based malware detection methods. The experimental results show that the detection accuracy of the malware detection method based on gray images and deep learning for the AEs generated by BAUAA is significantly lower than that for the non-AEs. To avoid the abuse of BAUAA in reality, it proposes a targeted AE detection method
Large Language Model Alignment: A Survey
Recent years have witnessed remarkable progress made in large language models
(LLMs). Such advancements, while garnering significant attention, have
concurrently elicited various concerns. The potential of these models is
undeniably vast; however, they may yield texts that are imprecise, misleading,
or even detrimental. Consequently, it becomes paramount to employ alignment
techniques to ensure these models to exhibit behaviors consistent with human
values.
This survey endeavors to furnish an extensive exploration of alignment
methodologies designed for LLMs, in conjunction with the extant capability
research in this domain. Adopting the lens of AI alignment, we categorize the
prevailing methods and emergent proposals for the alignment of LLMs into outer
and inner alignment. We also probe into salient issues including the models'
interpretability, and potential vulnerabilities to adversarial attacks. To
assess LLM alignment, we present a wide variety of benchmarks and evaluation
methodologies. After discussing the state of alignment research for LLMs, we
finally cast a vision toward the future, contemplating the promising avenues of
research that lie ahead.
Our aspiration for this survey extends beyond merely spurring research
interests in this realm. We also envision bridging the gap between the AI
alignment research community and the researchers engrossed in the capability
exploration of LLMs for both capable and safe LLMs.Comment: 76 page
High Precision Infrared Temperature Measurement System Based on Distance Compensation
To meet the need of real-time remote monitoring of human body surface temperature for optical rehabilitation therapy, a non-contact high-precision real-time temperature measurement method based on distance compensation was proposed, and the system design was carried out. The microcontroller controls the infrared temperature measurement module and the laser range module to collect temperature and distance data. The compensation formula of temperature with distance wass fitted according to the least square method. Testing had been performed on different individuals to verify the accuracy of the system. The results indicate that the designed non-contact infrared temperature measurement system has a residual error of less than 0.2°C and the response time isless than 0.1s in the range of 0 to 60cm. This provides a reference for developing long-distance temperature measurement equipment in optical rehabilitation therapy
Systematic Theoretical Analysis of Dual-Parameters R
This paper systematically studied the simultaneous measurement of two parameters by a LC-type passive sensor from the theoretical perspective. Based on the lumped circuit model of the typical LC-type passive dual-parameter sensor system, the influencing factors of the signal strength of the sensor as well as the influencing factors of signal crosstalk were both analyzed. It is found that the influencing factors of the RF readout signal strength of the sensor are mainly quality factors (Q factors) of the LC tanks, coupling coefficients, and the resonant frequency interval of the two LC tanks. And the influencing factors of the signal crosstalk are mainly coupling coefficient between the sensor inductance coils and the resonant frequency interval of the two LC tanks. The specific influence behavior of corresponding influencing factors on the signal strength and crosstalk is illustrated by a series of curves from numerical results simulated by using MATLAB software. Additionally, a decoupling scheme for solving the crosstalk problem algorithmically was proposed and a corresponding function was derived out. Overall, the theoretical analysis conducted in this work can provide design guidelines for making the dual-parameter LC-type passive sensor useful in practical applications
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