486 research outputs found
マダイ稚魚の成長、消化率、消化管形態、サイトカイン遺伝子の発現に対する低・無魚粉飼料へのタウリンの添加効果に関する研究
東京海洋大学博士学位論文 2019年度(2020年3月) 応用生物科学 課程博士 甲第536号指導教員:佐藤秀一東京海洋大学201
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems
Datasets for training recommender systems are often subject to distribution
shift induced by users' and recommenders' selection biases. In this paper, we
study the impact of selection bias on datasets with different quantization. We
then leverage two differently quantized datasets from different source
distributions to mitigate distribution shift by applying the inverse
probability scoring method from causal inference. Empirically, our approach
gains significant performance improvement over single-dataset methods and
alternative ways of combining two datasets.Comment: In Workshop on Distribution Shifts, 36th Conference on Neural
Information Processing Systems (NeurIPS 2022
Directed Greybox Fuzzing with Stepwise Constraint Focusing
Dynamic data flow analysis has been widely used to guide greybox fuzzing.
However, traditional dynamic data flow analysis tends to go astray in the
massive path tracking and requires to process a large volume of data, resulting
in low efficiency in reaching the target location. In this paper, we propose a
directed greybox fuzzer based on dynamic constraint filtering and focusing
(CONFF). First, all path constraints are tracked, and those with high priority
are filtered as the next solution targets. Next, focusing on a single path
constraint to be satisfied, we obtain its data condition and probe the mapping
relationship between it and the input bytes through multi-byte mapping and
single-byte mapping. Finally, various mutation strategies are utilized to solve
the path constraint currently focused on, and the target location of the
program is gradually approached through path selection. The CONFF fuzzer can
reach a specific location faster in the target program, thus efficiently
triggering the crash. We designed and implemented a prototype of the CONFF
fuzzer and evaluated it with the LAVA-1 dataset and some real-world
vulnerabilities. The results show that the CONFF fuzzer can reproduce crashes
on the LAVA-1 dataset and most of the real-world vulnerabilities. For most
vulnerabilities, the CONFF fuzzer reproduced the crashes with significantly
reduced time compared to state-of-the-art fuzzers. On average, the CONFF fuzzer
was 23.7x faster than the state-of-the-art code coverage-based fuzzer Angora
and 27.3x faster than the classical directed greybox fuzzer AFLGo
A Topology-Controlled Photonic Cavity Based on the Near-Conservation of the Valley Degree of Freedom
We demonstrate a novel path to localizing topologically-nontrivial photonic
edge modes along their propagation direction. Our approach is based on the
near-conservation of the photonic valley degree of freedom associated with
valley-polarized edge states. When the edge state is reflected from a
judiciously oriented mirror, its optical energy is localized at the mirror
surface because of an extended time delay required for valley-index-flipping.
The degree of energy localization at the resulting topology-controlled photonic
cavity (TCPC) is determined by the valley-flipping time, which is in turn
controlled by the geometry of the mirror. Intuitive analytic descriptions of
the "leaky" and closed TCPCs are presented, and two specific designs--one for
the microwave and the other for the optical spectral ranges--are proposed.Comment: 5 pages, 6 figure
Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for TDD MultiUser MIMO Systems
In this letter, we investigate the discrete phase shift design of the
intelligent reflecting surface (IRS) in a time division duplexing (TDD)
multi-user multiple input multiple output (MIMO) system.We modify the design of
deep reinforcement learning (DRL) scheme so that we can maximizing the average
downlink data transmission rate free from the sub-channel channel state
information (CSI). Based on the characteristics of the model, we modify the
proximal policy optimization (PPO) algorithm and integrate gated recurrent unit
(GRU) to tackle the non-convex optimization problem. Simulation results show
that the performance of the proposed PPO-GRU surpasses the benchmarks in terms
of performance, convergence speed, and training stability
Test-Time Distribution Normalization for Contrastively Learned Vision-language Models
Advances in the field of vision-language contrastive learning have made it
possible for many downstream applications to be carried out efficiently and
accurately by simply taking the dot product between image and text
representations. One of the most representative approaches proposed recently
known as CLIP has garnered widespread adoption due to its effectiveness. CLIP
is trained with an InfoNCE loss that takes into account both positive and
negative samples to help learn a much more robust representation space. This
paper reveals that the common downstream practice of taking a dot product is
only a zeroth-order approximation of the optimization goal, resulting in a loss
of information during test-time. Intuitively, since the model has been
optimized based on the InfoNCE loss, test-time procedures should also be in
alignment. The question lies in how one can retrieve any semblance of negative
samples information during inference in a computationally efficient way. To
this end, we propose Distribution Normalization (DN), where we approximate the
mean representation of a batch of test samples and use such a mean to represent
what would be analogous to negative samples in the InfoNCE loss. DN requires no
retraining or fine-tuning and can be effortlessly applied during inference.
Extensive experiments on a wide variety of downstream tasks exhibit a clear
advantage of DN over the dot product on top of other existing test-time
augmentation methods.Comment: Accepted to NeurIPS 2023, project webpage:
https://fengyuli-dev.github.io/dn-website
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