Learning to Optimize Domain Specific Normalization for Domain Generalization

We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per domain. For each domain, the activations are normalized by a weighted average of multiple normalization statistics. The normalization statistics are kept track of separately for each normalization type if necessary. Specifically, we employ batch and instance normalizations in our implementation to identify the best combination of these two normalization methods in each domain. The optimized normalization layers are effective to enhance the generalizability of the learned model. We demonstrate the state-of-the-art accuracy of our algorithm in the standard domain generalization benchmarks, as well as viability to further tasks such as multi-source domain adaptation and domain generalization in the presence of label noise

PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac

OmniLabel: A Challenging Benchmark for Language-Based Object Detection

Language-based object detection is a promising direction towards building a natural interface to describe objects in images that goes far beyond plain category names. While recent methods show great progress in that direction, proper evaluation is lacking. With OmniLabel, we propose a novel task definition, dataset, and evaluation metric. The task subsumes standard- and open-vocabulary detection as well as referring expressions. With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting. Moreover, a key differentiation to existing benchmarks is that our object descriptions can refer to one, multiple or even no object, hence, providing negative examples in free-form text. The proposed evaluation handles the large label space and judges performance via a modified average precision metric, which we validate by evaluating strong language-based baselines. OmniLabel indeed provides a challenging test bed for future research on language-based detection.Comment: ICCV 2023 Oral - Visit our project website at https://www.omnilabel.or

Improving Pseudo Labels for Open-Vocabulary Object Detection

Recent studies show promising performance in open-vocabulary object detection (OVD) using pseudo labels (PLs) from pretrained vision and language models (VLMs). However, PLs generated by VLMs are extremely noisy due to the gap between the pretraining objective of VLMs and OVD, which blocks further advances on PLs. In this paper, we aim to reduce the noise in PLs and propose a method called online Self-training And a Split-and-fusion head for OVD (SAS-Det). First, the self-training finetunes VLMs to generate high quality PLs while prevents forgetting the knowledge learned in the pretraining. Second, a split-and-fusion (SAF) head is designed to remove the noise in localization of PLs, which is usually ignored in existing methods. It also fuses complementary knowledge learned from both precise ground truth and noisy pseudo labels to boost the performance. Extensive experiments demonstrate SAS-Det is both efficient and effective. Our pseudo labeling is 3 times faster than prior methods. SAS-Det outperforms prior state-of-the-art models of the same scale by a clear margin and achieves 37.4 AP$_{50}$ and 27.3 AP$_r$ on novel categories of the COCO and LVIS benchmarks, respectively.Comment: 20 pages, 8 figure

Roles of Time Hazard in Perceptual Decision Making under High Time Pressure

The drift diffusion model (DDM) has been successful in capturing the joint dynamics of accuracy and latency data in various perceptual decision making tasks. We evaluated how well the DDM describes dynamics of perceptual decision when subjects were under a varying degree of time pressure. We collected choice and latency responses from human subjects, who discriminated the size of a thin ring stimulus with a varying degree of uncertainty. The degree of time pressure was manipulated both by giving subjects an explicit instruction of different time limits across sessions (0.7 ∼ 1.2 s) and by providing feedback to responses that were made later than those time limits. When fitted to the data of choice and latency, the three major variants of the DDM (with static bounds & gain, with time-varying bounds, and with time-varying gain) showed a systematic pattern of latency-dependent prediction errors. Here we propose a new variant of the DDM, which adopts a ‘boundary for time hazard’ on the time axis in addition to the choice boundary on the choice-evidence axis in decision space. Our model did not exhibit the biased pattern of errors and was superior than the other models in goodness of fit to the data

Antiproliferative Acetogenins from a <i>Uvaria</i> sp. from the Madagascar Dry Forest

Investigation of the endemic Madagascan plant <i>Uvaria </i>sp<i>.</i> for antiproliferative activity against the A2780 ovarian cancer cell line led to the isolation of two new acetogenins. The structures of these two compounds were elucidated on the basis of analysis of their 1D and 2D NMR spectra, circular dichroism, and mass spectrometric data, together with chemical modification. The two acetogenins display weak antiproliferative activity against the A2780 ovarian cancer, the A2058 melanoma, and the H522 lung cancer cell lines

Voltage control of magnetism in Fe3-x GeTe2/In2Se3 van der Waals ferromagnetic/ferroelectric heterostructures

Abstract We investigate the voltage control of magnetism in a van der Waals (vdW) heterostructure device consisting of two distinct vdW materials, the ferromagnetic Fe3-x GeTe2 and the ferroelectric In2Se3. It is observed that gate voltages applied to the Fe3-x GeTe2/In2Se3 heterostructure device modulate the magnetic properties of Fe3-x GeTe2 with significant decrease in coercive field for both positive and negative voltages. Raman spectroscopy on the heterostructure device shows voltage-dependent increase in the in-plane In2Se3 and Fe3-x GeTe2 lattice constants for both voltage polarities. Thus, the voltage-dependent decrease in the Fe3-x GeTe2 coercive field, regardless of the gate voltage polarity, can be attributed to the presence of in-plane tensile strain. This is supported by density functional theory calculations showing tensile-strain-induced reduction of the magnetocrystalline anisotropy, which in turn decreases the coercive field. Our results demonstrate an effective method to realize low-power voltage-controlled vdW spintronic devices utilizing the magnetoelectric effect in vdW ferromagnetic/ferroelectric heterostructures

Theoretical study of carrier transport and break down behavior of wide band gap semiconductors and related devices

Issued as final repor