Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models. The approach introduces a discriminator that gives explicit supervision to a denoising sample path whether it is realistic or not. Unlike GANs, our approach does not require joint training of score and discriminator networks. Instead, we train the discriminator after score training, making discriminator training stable and fast to converge. In sample generation, we add an auxiliary term to the pre-trained score to deceive the discriminator. This term corrects the model score to the data score at the optimal discriminator, which implies that the discriminator helps better score estimation in a complementary way. Using our algorithm, we achive state-of-the-art results on ImageNet 256x256 with FID 1.83 and recall 0.64, similar to the validation data's FID (1.68) and recall (0.66). We release the code at https://github.com/alsdudrla10/DG.Comment: International Conference on Machine Learning (ICML23

AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection

Unsupervised anomaly detection is coming into the spotlight these days in various practical domains due to the limited amount of anomaly data. One of the major approaches for it is a normalizing flow which pursues the invertible transformation of a complex distribution as images into an easy distribution as N(0, I). In fact, algorithms based on normalizing flow like FastFlow and CFLOW-AD establish state-of-the-art performance on unsupervised anomaly detection tasks. Nevertheless, we investigate these algorithms convert normal images into not N(0, I) as their destination, but an arbitrary normal distribution. Moreover, their performances are often unstable, which is highly critical for unsupervised tasks because data for validation are not provided. To break through these observations, we propose a simple solution AltUB which introduces alternating training to update the base distribution of normalizing flow for anomaly detection. AltUB effectively improves the stability of performance of normalizing flow. Furthermore, our method achieves the new state-of-the-art performance of the anomaly segmentation task on the MVTec AD dataset with 98.8% AUROC.Comment: 9 pages, 4 figure

Multi-User Security of the Sum of Truncated Random Permutations (Full Version)

For several decades, constructing pseudorandom functions from pseudorandom permutations, so-called Luby-Rackoff backward construction, has been a popular cryptographic problem. Two methods are well-known and comprehensively studied for this problem: summing two random permutations and truncating partial bits of the output from a random permutation. In this paper, by combining both summation and truncation, we propose new Luby-Rackoff backward constructions, dubbed SaT1 and SaT2, respectively. SaT2 is obtained by partially truncating output bits from the sum of two independent random permutations, and SaT1 is its single permutation-based variant using domain separation. The distinguishing advantage against SaT1 and SaT2 is upper bounded by O(\sqrt{\mu q_max}/2^{n-0.5m}) and O({\sqrt{\mu}q_max^1.5}/2^{2n-0.5m}), respectively, in the multi-user setting, where n is the size of the underlying permutation, m is the output size of the construction, \mu is the number of users, and q_max is the maximum number of queries per user. We also prove the distinguishing advantage against a variant of XORP[3]~(studied by Bhattacharya and Nandi at Asiacrypt 2021) using independent permutations, dubbed SoP3-2, is upper bounded by O(\sqrt{\mu} q_max^2}/2^{2.5n})$. In the multi-user setting with \mu = O(2^{n-m}), a truncated random permutation provides only the birthday bound security, while SaT1 and SaT2 are fully secure, i.e., allowing O(2^n) queries for each user. It is the same security level as XORP[3] using three permutation calls, while SaT1 and SaT2 need only two permutation calls

Label-Noise Robust Diffusion Models

Conditional diffusion models have shown remarkable performance in various generative tasks, but training them requires large-scale datasets that often contain noise in conditional inputs, a.k.a. noisy labels. This noise leads to condition mismatch and quality degradation of generated data. This paper proposes Transition-aware weighted Denoising Score Matching (TDSM) for training conditional diffusion models with noisy labels, which is the first study in the line of diffusion models. The TDSM objective contains a weighted sum of score networks, incorporating instance-wise and time-dependent label transition probabilities. We introduce a transition-aware weight estimator, which leverages a time-dependent noisy-label classifier distinctively customized to the diffusion process. Through experiments across various datasets and noisy label settings, TDSM improves the quality of generated samples aligned with given conditions. Furthermore, our method improves generation performance even on prevalent benchmark datasets, which implies the potential noisy labels and their risk of generative model learning. Finally, we show the improved performance of TDSM on top of conventional noisy label corrections, which empirically proving its contribution as a part of label-noise robust generative models. Our code is available at: https://github.com/byeonghu-na/tdsm.Comment: Accepted at ICLR 202

Forking Tweakable Even-Mansour Ciphers

A forkcipher is a keyed, tweakable function mapping an n-bit input to a 2nbit output, which is equivalent to concatenating two outputs from two permutations. A forkcipher can be a useful primitive to design authenticated encryption schemes for short messages. A forkcipher is typically designed within the iterate-fork-iterate (IFI) paradigm, while the provable security of such a construction has not been widely explored.In this paper, we propose a method of constructing a forkcipher using public permutations as its building primitives. It can be seen as applying the IFI paradigm to the tweakable Even-Mansour ciphers. So our construction is dubbed the forked tweakable Even-Mansour (FTEM) cipher. Our main result is to prove that a (1, 1)-round FTEM cipher (applying a single-round TEM to a plaintext, followed by two independent copies of a single-round TEM) is secure up to 2 2n/3 queries in the ideal permutation model

What Attributes Do Passengers Value in Electrified Buses?

The Korean government has announced plans to supply electrified buses to achieve decarbonization in the transportation sector and to create next-generation growth engines. Although a multitude of technical and political studies have been conducted to support the successful introduction of electrified buses, studies on the attitudes and perceptions of passengers toward electrified buses remain insufficient. To evaluate the perceptions and preferences of potential passengers toward the specific attributes of electrified buses, this study performed an online survey (N = 586) that includes people who had experienced travel on public buses. Values of the relative importance of eight different attributes—safety, ride comfort, environmental friendliness, exterior design, cleanliness, crowding, seat comfort, and convenience getting on/off—were evaluated using the best-worst scaling method. The results showed that safety (share of preference: 41.3%) was the most important attribute when using electrified buses. This was followed by eco-friendliness (14.3%) and ride comfort (13.6%). On the other hand, the least important attribute was exterior design (1.8%). Gender differences were also observed in the valuation of certain attributes among the passenger preferences toward electrified buses. The results of this study contribute to the development of strategies for the wide-spread adoption of electrified buses and provide a stepping-stone to a more sustainable public transportation system

SCOPE OF VARIOUS SOLVENTS AND THEIR EFFECTS ON SOLVOTHERMAL SYNTHESIS OF Ni-BTC

Ni-BTC (BTC = 1,3,5-benzene tricarboxylate) metal organic framework (MOF) was synthesized using different solvent conditions. Solvent mixtures of water/N,N-dimethylformamide (DMF), water/ethanol, and water/ethanol/DMF were used for the reactions with or without a variety of bases at 160 ºC for 48 hours. Even with same green crystals, prepared MOFs show all different BET surface areas and different XRD patterns. The highest BET surface area of the crystals was 850 m2/g obtained from water/DMF solvent with NH4OH as a base. The measured surface areas of the crystals follows the order of Ni-BTC(water/DMF-NH4OH) > Ni-BTC(water/DMF-TMA) > Ni-BTC(water/DMF) > Ni-BTC(water/DMF-Pyridine)> Ni-BTC(water/ethanol)> Ni-BTC(water/DMF-aniline)> Ni-BTC(water/DMF-NaOH)

Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines

In this paper, a high-power amplifier integrated circuit (IC) in gallium-nitride (GaN) on silicon (Si) technology is presented at a W-band (75–110 GHz). In order to mitigate the losses caused by relatively high loss tangent of Si substrate compared to silicon carbide (SiC), low-impedance microstrip lines (20–30 Ω) are adopted in the impedance matching networks. They allow for the impedance transformation between 50 Ω and very low impedances of the wide-gate transistors used for high power generation. Each stage is matched to produce enough power to drive the next stage. A Lange coupler is employed to combine two three-stage common source amplifiers, providing high output power and good input/output return loss. The designed power amplifier IC was fabricated in the commercially available 60 nm GaN-on-Si high electron mobility transistor (HEMT) foundry. From on-wafer probe measurements, it exhibits the output power higher than 26.5 dBm and power added efficiency (PAE) higher than 8.5% from 88 to 93 GHz with a large-signal gain > 10.5 dB. Peak output power is measured to be 28.9 dBm with a PAE of 13.3% and a gain of 9.9 dB at 90 GHz, which corresponds to the power density of 1.94 W/mm. To the best of the authors’ knowledge, this result belongs to the highest output power and power density among the reported power amplifier ICs in GaN-on-Si HEMT technologies operating at the W-band