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Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback.

Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into consideration the homography of words. In this study, we incorporate the Wikipedia knowledge into a neural topic model to make it aware of named entities. We evaluate our method on two datasets, 1) news articles of \textit{New York Times} and 2) the AIDA-CoNLL dataset. Our experiments show that our method improves the performance of neural topic models in generalizability. Moreover, we analyze frequent terms in each topic and the temporal dependencies between topics to demonstrate that our entity-aware topic models can capture the time-series development of topics well., Comment: Accepted at EMNLP 2024 Workshop NLP for Wikipedia

In this work, we build a simple but strong baseline for sounding video generation. Given base diffusion models for audio and video, we integrate them with additional modules into a single model and train it to make the model jointly generate audio and video. To enhance alignment between audio-video pairs, we introduce two novel mechanisms in our model. The first one is timestep adjustment, which provides different timestep information to each base model. It is designed to align how samples are generated along with timesteps across modalities. The second one is a new design of the additional modules, termed Cross-Modal Conditioning as Positional Encoding (CMC-PE). In CMC-PE, cross-modal information is embedded as if it represents temporal position information, and the embeddings are fed into the model like positional encoding. Compared with the popular cross-attention mechanism, CMC-PE provides a better inductive bias for temporal alignment in the generated data. Experimental results validate the effectiveness of the two newly introduced mechanisms and also demonstrate that our method outperforms existing methods., Comment: The source code will be released soon

We propose an algorithm for counting the number of cycles under local differential privacy for degeneracy-bounded input graphs. Numerous studies have focused on counting the number of triangles under the privacy notion, demonstrating that the expected $\ell_2$-error of these algorithms is $\Omega(n^{1.5})$, where $n$ is the number of nodes in the graph. When parameterized by the number of cycles of length four ($C_4$), the best existing triangle counting algorithm has an error of $O(n^{1.5} + \sqrt{C_4}) = O(n^2)$. In this paper, we introduce an algorithm with an expected $\ell_2$-error of $O(\delta^{1.5} n^{0.5} + \delta^{0.5} d_{\max}^{0.5} n^{0.5})$, where $\delta$ is the degeneracy and $d_{\max}$ is the maximum degree of the graph. For degeneracy-bounded graphs ($\delta \in \Theta(1)$) commonly found in practical social networks, our algorithm achieves an expected $\ell_2$-error of $O(d_{\max}^{0.5} n^{0.5}) = O(n)$. Our algorithm's core idea is a precise count of triangles following a preprocessing step that approximately sorts the degree of all nodes. This approach can be extended to approximate the number of cycles of length $k$, maintaining a similar $\ell_2$-error, namely $O(\delta^{(k-2)/2} d_{\max}^{0.5} n^{(k-2)/2} + \delta^{k/2} n^{(k-2)/2})$ or $O(d_{\max}^{0.5} n^{(k-2)/2}) = O(n^{(k-1)/2})$ for degeneracy-bounded graphs.

Recently, object detection methods (OD; e.g., YOLO-based models) have been widely utilized in plant disease diagnosis. These methods demonstrate robustness to distance variations and excel at detecting small lesions compared to classification methods (CL; e.g., CNN models). However, there are issues such as low diagnostic performance for hard-to-detect diseases and high labeling costs. Additionally, since healthy cases cannot be explicitly trained, there is a risk of false positives. We propose the Hierarchical object detection and recognition framework (HODRF), a sophisticated and highly integrated two-stage system that combines the strengths of both OD and CL for plant disease diagnosis. In the first stage, HODRF uses OD to identify regions of interest (ROIs) without specifying the disease. In the second stage, CL diagnoses diseases surrounding the ROIs. HODRF offers several advantages: (1) Since OD detects only one type of ROI, HODRF can detect diseases with limited training images by leveraging its ability to identify other lesions. (2) While OD over-detects healthy cases, HODRF significantly reduces these errors by using CL in the second stage. (3) CL's accuracy improves in HODRF as it identifies diagnostic targets given as ROIs, making it less vulnerable to size changes. (4) HODRF benefits from CL's lower annotation costs, allowing it to learn from a larger number of images. We implemented HODRF using YOLOv7 for OD and EfficientNetV2 for CL and evaluated its performance on a large-scale dataset (4 crops, 20 diseased and healthy classes, 281K images). HODRF outperformed YOLOv7 alone by 5.8 to 21.5 points on healthy data and 0.6 to 7.5 points on macro F1 scores, and it improved macro F1 by 1.1 to 7.2 points over EfficientNetV2., Comment: 6 pages, 3 figures

Recent advances in generative models that iteratively synthesize audio clips sparked great success to text-to-audio synthesis (TTA), but with the cost of slow synthesis speed and heavy computation. Although there have been attempts to accelerate the iterative procedure, high-quality TTA systems remain inefficient due to hundreds of iterations required in the inference phase and large amount of model parameters. To address the challenges, we propose SpecMaskGIT, a light-weighted, efficient yet effective TTA model based on the masked generative modeling of spectrograms. First, SpecMaskGIT synthesizes a realistic 10s audio clip by less than 16 iterations, an order-of-magnitude less than previous iterative TTA methods. As a discrete model, SpecMaskGIT outperforms larger VQ-Diffusion and auto-regressive models in the TTA benchmark, while being real-time with only 4 CPU cores or even 30x faster with a GPU. Next, built upon a latent space of Mel-spectrogram, SpecMaskGIT has a wider range of applications (e.g., the zero-shot bandwidth extension) than similar methods built on the latent wave domain. Moreover, we interpret SpecMaskGIT as a generative extension to previous discriminative audio masked Transformers, and shed light on its audio representation learning potential. We hope our work inspires the exploration of masked audio modeling toward further diverse scenarios., Comment: 6 pages, 8 figures, 8 tables. Audio samples: https://zzaudio.github.io/SpecMaskGIT/index.html

In motion generation, controllability as well as generation quality and speed is becoming more and more important. There are various motion editing tasks, such as in-betweening, upper body editing, and path-following, but existing methods perform motion editing with a data-space diffusion model, which is slow in inference compared to a latent diffusion model. In this paper, we propose MoLA, which provides fast and high-quality motion generation and also can deal with multiple editing tasks in a single framework. For high-quality and fast generation, we employ a variational autoencoder and latent diffusion model, and improve the performance with adversarial training. In addition, we apply a training-free guided generation framework to achieve various editing tasks with motion control inputs. We quantitatively show the effectiveness of adversarial learning in text-to-motion generation, and demonstrate the applicability of our editing framework to multiple editing tasks in the motion domain., Comment: 12 pages, 6 figures

In this study, we aim to construct an audio-video generative model with minimal computational cost by leveraging pre-trained single-modal generative models for audio and video. To achieve this, we propose a novel method that guides each single-modal model to cooperatively generate well-aligned samples across modalities. Specifically, given two pre-trained base diffusion models, we train a lightweight joint guidance module to adjust scores separately estimated by the base models to match the score of joint distribution over audio and video. We theoretically show that this guidance can be computed through the gradient of the optimal discriminator distinguishing real audio-video pairs from fake ones independently generated by the base models. On the basis of this analysis, we construct the joint guidance module by training this discriminator. Additionally, we adopt a loss function to make the gradient of the discriminator work as a noise estimator, as in standard diffusion models, stabilizing the gradient of the discriminator. Empirical evaluations on several benchmark datasets demonstrate that our method improves both single-modal fidelity and multi-modal alignment with a relatively small number of parameters.

Sound content is an indispensable element for multimedia works such as video games, music, and films. Recent high-quality diffusion-based sound generation models can serve as valuable tools for the creators. However, despite producing high-quality sounds, these models often suffer from slow inference speeds. This drawback burdens creators, who typically refine their sounds through trial and error to align them with their artistic intentions. To address this issue, we introduce Sound Consistency Trajectory Models (SoundCTM). Our model enables flexible transitioning between high-quality 1-step sound generation and superior sound quality through multi-step generation. This allows creators to initially control sounds with 1-step samples before refining them through multi-step generation. While CTM fundamentally achieves flexible 1-step and multi-step generation, its impressive performance heavily depends on an additional pretrained feature extractor and an adversarial loss, which are expensive to train and not always available in other domains. Thus, we reframe CTM's training framework and introduce a novel feature distance by utilizing the teacher's network for a distillation loss. Additionally, while distilling classifier-free guided trajectories, we train conditional and unconditional student models simultaneously and interpolate between these models during inference. We also propose training-free controllable frameworks for SoundCTM, leveraging its flexible sampling capability. SoundCTM achieves both promising 1-step and multi-step real-time sound generation without using any extra off-the-shelf networks. Furthermore, we demonstrate SoundCTM's capability of controllable sound generation in a training-free manner. Our codes, pretrained models, and audio samples are available at https://github.com/sony/soundctm., Comment: Audio samples: https://koichi-saito-sony.github.io/soundctm/. Codes: https://github.com/sony/soundctm. Checkpoints: https://huggingface.co/Sony/soundctm

Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth estimation (MDE) has shown promise in handling in-the-wild images. In these methods, an input view is geometrically warped to novel views with estimated depth maps, then the warped image is inpainted by T2I models. However, they struggle with noisy depth maps and loss of semantic details when warping an input view to novel viewpoints. In this paper, we propose a novel approach for single-shot novel view synthesis, a semantic-preserving generative warping framework that enables T2I generative models to learn where to warp and where to generate, through augmenting cross-view attention with self-attention. Our approach addresses the limitations of existing methods by conditioning the generative model on source view images and incorporating geometric warping signals. Qualitative and quantitative evaluations demonstrate that our model outperforms existing methods in both in-domain and out-of-domain scenarios. Project page is available at https://GenWarp-NVS.github.io/., Comment: Accepted to NeurIPS 2024 / Project page: https://GenWarp-NVS.github.io

In recent years, with the realistic generation results and a wide range of personalized applications, diffusion-based generative models gain huge attention in both visual and audio generation areas. Compared to the considerable advancements of text2image or text2audio generation, research in audio2visual or visual2audio generation has been relatively slow. The recent audio-visual generation methods usually resort to huge large language model or composable diffusion models. Instead of designing another giant model for audio-visual generation, in this paper we take a step back showing a simple and lightweight generative transformer, which is not fully investigated in multi-modal generation, can achieve excellent results on image2audio generation. The transformer operates in the discrete audio and visual Vector-Quantized GAN space, and is trained in the mask denoising manner. After training, the classifier-free guidance could be deployed off-the-shelf achieving better performance, without any extra training or modification. Since the transformer model is modality symmetrical, it could also be directly deployed for audio2image generation and co-generation. In the experiments, we show that our simple method surpasses recent image2audio generation methods. Generated audio samples can be found at https://docs.google.com/presentation/d/1ZtC0SeblKkut4XJcRaDsSTuCRIXB3ypxmSi7HTY3IyQ/, Comment: 10 pages

We super-resolve the seeing-limited Subaru Hyper Suprime-Cam (HSC) images for 32,187 galaxies at z~2-5 in three techniques, namely, the classical Richardson-Lucy (RL) point spread function (PSF) deconvolution, sparse modeling, and generative adversarial networks to investigate the environmental dependence of galaxy mergers. These three techniques generate overall similar high spatial resolution images but with some slight differences in galaxy structures, for example, more residual noises are seen in the classical RL PSF deconvolution. To alleviate disadvantages of each technique, we create combined images by averaging over the three types of super-resolution images, which result in galaxy sub-structures resembling those seen in the Hubble Space Telescope images. Using the combined super-resolution images, we measure the relative galaxy major merger fraction corrected for the chance projection effect, f_merg, for galaxies in the ~300 deg^2-area data of the HSC Strategic Survey Program and the CFHT Large Area U-band Survey. Our f_merg measurements at z~3 validate previous findings showing that f_merg is higher in regions with a higher galaxy overdensity delta at z~2-3. Thanks to the large galaxy sample, we identify a nearly linear increase in f_merg with increasing delta at z~4-5, providing the highest-z observational evidence that galaxy mergers are related to delta. In addition to our f_merg measurements, we find that the galaxy merger fractions in the literature also broadly align with the linear f_merg-delta relation across a wide redshift range of z~2-5. This alignment suggests that the linear f_merg-delta relation can serve as a valuable tool for quantitatively estimating the contributions of galaxy mergers to various environmental dependences. This super-resolution analysis can be readily applied to datasets from wide field-of-view space telescopes such as Euclid and Roman., Comment: 29 pages, 13 figures, 6 tables. Submitted to PASJ. Comments welcome

Early galaxy formation, initiated by the dark matter and gas assembly, evolves through frequent mergers and feedback processes into dynamically hot, chaotic structures. In contrast, dynamically cold, smooth rotating disks have been observed in massive evolved galaxies merely 1.4 billion years after the Big Bang, suggesting rapid morphological and dynamical evolution in the early Universe. Probing this evolution mechanism necessitates studies of young galaxies, yet efforts have been hindered by observational limitations in both sensitivity and spatial resolution. Here we report high-resolution observations of a strongly lensed and quintuply imaged, low-luminosity, young galaxy at $z=6.072$ (dubbed the Cosmic Grapes), 930 million years after the Big Bang. Magnified by gravitational lensing, the galaxy is resolved into at least 15 individual star-forming clumps with effective radii of $r_{\rm e}\simeq$ 10--60 parsec (pc), which dominate $\simeq$ 70\% of the galaxy's total flux. The cool gas emission unveils a smooth, underlying rotating disk characterized by a high rotational-to-random motion ratio and a gravitationally unstable state (Toomre $Q \simeq$ 0.2--0.3), with high surface gas densities comparable to local dusty starbursts with $\simeq10^{3-5}$ $M_{\odot}$/pc$^{2}$. These gas properties suggest that the numerous star-forming clumps are formed through disk instabilities with weak feedback effects. The clumpiness of the Cosmic Grapes significantly exceeds that of galaxies at later epochs and the predictions from current simulations for early galaxies. Our findings shed new light on internal galaxy substructures and their relation to the underlying dynamics and feedback mechanisms at play during their early formation phases, potentially explaining the high abundance of bright galaxies observed in the early Universe and the dark matter core-cusp problem., Comment: Submitted. 44 pages, 14 figures, 5 tables. Comments are welcome! See also the companion papers on arXiv. Valentino+2024: arXiv:2402.17845 Gim\'enez-Arteaga+2024: arXiv:2402.17875

With the increasing amount of data in society, privacy concerns in data sharing have become widely recognized. Particularly, protecting personal attribute information is essential for a wide range of aims from crowdsourcing to realizing personalized medicine. Although various differentially private methods based on randomized response have been proposed for single attribute information or specific analysis purposes such as frequency estimation, there is a lack of studies on the mechanism for sharing individuals' multiple categorical information itself. The existing randomized response for sharing multi-attribute data uses the Kronecker product to perturb each attribute information in turn according to the respective privacy level but achieves only a weak privacy level for the entire dataset. Therefore, in this study, we propose a privacy-optimized randomized response that guarantees the strongest privacy in sharing multi-attribute data. Furthermore, we present an efficient heuristic algorithm for constructing a near-optimal mechanism. The time complexity of our algorithm is O(k^2), where k is the number of attributes, and it can be performed in about 1 second even for large datasets with k = 1,000. The experimental results demonstrate that both of our methods provide significantly stronger privacy guarantees for the entire dataset than the existing method. In addition, we show an analysis example using genome statistics to confirm that our methods can achieve less than half the output error compared with that of the existing method. Overall, this study is an important step toward trustworthy sharing and analysis of multi-attribute data. The Python implementation of our experiments and supplemental results are available at https://github.com/ay0408/Optimized-RR.

Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model, VQ-VAE, which can be further extended to hierarchical structures for making high-fidelity reconstructions. However, such hierarchical extensions of VQ-VAE often suffer from the codebook/layer collapse issue, where the codebook is not efficiently used to express the data, and hence degrades reconstruction accuracy. To mitigate this problem, we propose a novel unified framework to stochastically learn hierarchical discrete representation on the basis of the variational Bayes framework, called hierarchically quantized variational autoencoder (HQ-VAE). HQ-VAE naturally generalizes the hierarchical variants of VQ-VAE, such as VQ-VAE-2 and residual-quantized VAE (RQ-VAE), and provides them with a Bayesian training scheme. Our comprehensive experiments on image datasets show that HQ-VAE enhances codebook usage and improves reconstruction performance. We also validated HQ-VAE in terms of its applicability to a different modality with an audio dataset., Comment: 34 pages with 17 figures, accepted for TMLR

We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing graph statistics, including the count of subgraphs, under the edge local differential privacy, many suffer with high communication costs, making them less efficient for large graphs. Though data compression is a typical approach in differential privacy, its application in local differential privacy requires a form of compression that every node can reproduce. In our study, we introduce linear congruence hashing. With a sampling rate of $s$, our method can cut communication costs by a factor of $s^2$, albeit at the cost of increasing variance in the published graph statistic by a factor of $s$. The experimental results indicate that, when matched for communication costs, our method achieves a reduction in the $\ell_2$-error for triangle counts by up to 1000 times compared to the performance of leading algorithms., Comment: 13 pages, 3 figures

We present six strongly gravitationally lensed Ly-$\alpha$ Emitters (LAEs) at $z\sim4-5$ with HST narrowband imaging isolating Ly-$\alpha$. Through complex radiative transfer Ly-$\alpha$ encodes information about the spatial distribution and kinematics of the neutral hydrogen upon which it scatters. We investigate the galaxy properties and Ly-$\alpha$ morphologies of our sample. Many previous studies of high-redshift LAEs have been limited in Ly-$\alpha$ spatial resolution. In this work we take advantage of high-resolution Ly-$\alpha$ imaging boosted by lensing magnification, allowing us to probe sub-galactic scales that are otherwise inaccessible at these redshifts. We use broadband imaging from HST (rest-frame UV) and Spitzer (rest-frame optical) in SED fitting; providing estimates on the stellar masses ($\sim 10^8 - 10^9 M_{\odot}$), stellar population ages ($t_{50} <40$ Myr), and amounts of dust ($A_V \sim 0.1 - 0.6$, statistically consistent with zero). We employ non-parametric star-formation histories to probe the young stellar-populations which create Ly-$\alpha$. We also examine the offsets between the Ly-$\alpha$ and stellar continuum, finding small upper limits of offsets ($< 0.1"$) consistent with studies of low-redshift LAEs; indicating our galaxies are not interacting or merging. Finally, we find a bimodality in our sample's Ly-$\alpha$ morphologies: clumpy and extended. We find a suggestive trend: our LAEs with clumpy Ly-$\alpha$ are generally younger than the LAEs with extended Ly-$\alpha$, suggesting a possible correlation with age., Comment: 17 pages (2 for references), 5 figures, 6 tables

Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks. However, there has been limited investigation of and improvement in their language encoder, which is the central component of encoding natural language descriptions of image/audio into vector representations. We extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance. In VL pretraining, we found that sentence embedding training language encoder quality and aids in cross-modal tasks, improving contrastive VL models such as CyCLIP. In contrast, AL pretraining benefits less from sentence embedding training, which may result from the limited amount of pretraining data. We analyze the representation spaces to understand the strengths of sentence embedding training, and find that it improves text-space uniformity, at the cost of decreased cross-modal alignment.

Sound event localization and detection (SELD) systems estimate direction-of-arrival (DOA) and temporal activation for sets of target classes. Neural network (NN)-based SELD systems have performed well in various sets of target classes, but they only output the DOA and temporal activation of preset classes trained before inference. To customize target classes after training, we tackle zero- and few-shot SELD tasks, in which we set new classes with a text sample or a few audio samples. While zero-shot sound classification tasks are achievable by embedding from contrastive language-audio pretraining (CLAP), zero-shot SELD tasks require assigning an activity and a DOA to each embedding, especially in overlapping cases. To tackle the assignment problem in overlapping cases, we propose an embed-ACCDOA model, which is trained to output track-wise CLAP embedding and corresponding activity-coupled Cartesian direction-of-arrival (ACCDOA). In our experimental evaluations on zero- and few-shot SELD tasks, the embed-ACCDOA model showed better location-dependent scores than a straightforward combination of the CLAP audio encoder and a DOA estimation model. Moreover, the proposed combination of the embed-ACCDOA model and CLAP audio encoder with zero- or few-shot samples performed comparably to an official baseline system trained with complete train data in an evaluation dataset., Comment: 5 pages, 4 figures, accepted for publication in IEEE ICASSP 2024

Generative adversarial network (GAN)-based vocoders have been intensively studied because they can synthesize high-fidelity audio waveforms faster than real-time. However, it has been reported that most GANs fail to obtain the optimal projection for discriminating between real and fake data in the feature space. In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task. In this paper, we investigate the effectiveness of SAN in the vocoding task. For this purpose, we propose a scheme to modify least-squares GAN, which most GAN-based vocoders adopt, so that their loss functions satisfy the requirements of SAN. Through our experiments, we demonstrate that SAN can improve the performance of GAN-based vocoders, including BigVGAN, with small modifications. Our code is available at https://github.com/sony/bigvsan., Comment: Accepted at ICASSP 2024. Equation (5) in the previous version is wrong. We modified it

We present the rest-frame optical and UV surface brightness (SB) profiles for $149$ galaxies with $M_{\rm opt}< -19.4$ mag at $z=4$-$10$ ($29$ of which are spectroscopically confirmed with JWST NIRSpec), securing high signal-to-noise ratios of $10$-$135$ with deep JWST NIRCam $1$-$5\mu$m images obtained by the CEERS survey. We derive morphologies of our high-$z$ galaxies, carefully evaluating the systematics of SB profile measurements with Monte Carlo simulations as well as the impacts of a) AGNs, b) multiple clumps including galaxy mergers, c) spatial resolution differences with previous HST studies, and d) strong emission lines, e.g., H$\alpha$ and [OIII], on optical morphologies with medium-band F410M images. Conducting S\'ersic profile fitting to our high-$z$ galaxy SBs with GALFIT, we obtain the effective radii of optical $r_{\rm e, opt}$ and UV $r_{\rm e, UV}$ wavelengths ranging $r_{\rm e, opt}=0.05$-$1.6$ kpc and $r_{\rm e, UV}=0.03$-$1.7$ kpc that are consistent with previous results within large scatters in the size luminosity relations. However, we find the effective radius ratio, $r_{\rm e, opt}/r_{\rm e, UV}$, is almost unity, $1.01^{+0.35}_{-0.22}$, over $z=4$-$10$ with no signatures of past inside-out star formation such found at $z\sim 0$-$2$. There are no spatial offsets exceeding $3\sigma$ between the optical and UV morphology centers in case of no mergers, indicative of major star-forming activity only found near a mass center of galaxies at $z\gtrsim 4$ probably experiencing the first phase of inside-out galaxy formation., Comment: 33 pages, 18 figures, 6 tables, accepted

The Higgs potentials of extended Higgs sectors exhibit a complex and interesting vacuum structure. When travelling back in time, i.e. going to higher temperatures, the structure may change and exhibit interesting phase patterns and sequences of phases related to the respective minima of the potential. The investigation of the vacuum structure can give us indirect insights in beyond-Standard-Model physics and the evolution of the Universe. In this paper, we investigate the possibility of an intermediate charge-breaking (CB) phase in the 2-Higgs-Doublet Model (2HDM) type I. The existence has been reported previously by using a simple potential setup. We here confirm that the intermediate CB phase can still exist when using the one-loop corrected effective potential including thermal masses. We discuss its features and the relation with SU(2) symmetry (non-)restoration as well as its consistency with the current experimental data. Lastly, we show for some selected benchmark points the rich and interesting phase patterns and sequences that the 2HDM can undergo during its evolution from the early Universe to today's electroweak vacuum., Comment: 39 pages, 7 figures, 3 tables; references added, improved presentation of results, added appendix B; matches version published in JHEP

Diffusion-based generative speech enhancement (SE) has recently received attention, but reverse diffusion remains time-consuming. One solution is to initialize the reverse diffusion process with enhanced features estimated by a predictive SE system. However, the pipeline structure currently does not consider for a combined use of generative and predictive decoders. The predictive decoder allows us to use the further complementarity between predictive and diffusion-based generative SE. In this paper, we propose a unified system that use jointly generative and predictive decoders across two levels. The encoder encodes both generative and predictive information at the shared encoding level. At the decoded feature level, we fuse the two decoded features by generative and predictive decoders. Specifically, the two SE modules are fused in the initial and final diffusion steps: the initial fusion initializes the diffusion process with the predictive SE to improve convergence, and the final fusion combines the two complementary SE outputs to enhance SE performance. Experiments conducted on the Voice-Bank dataset demonstrate that incorporating predictive information leads to faster decoding and higher PESQ scores compared with other score-based diffusion SE (StoRM and SGMSE+).

We present 20,567 Ly$\alpha$ emitters (LAEs) at $z=2.2-7.3$ that are photometrically identified by the SILVERRUSH program in a large survey area up to 25 deg$^2$ with deep images of five broadband filters (grizy) and seven narrowband filters targeting Ly$\alpha$ lines at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, $7.0$, and $7.3$ taken by the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) and the Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS) survey. We select secure $>5\sigma$ sources showing narrowband color excesses via Ly$\alpha$ break screening, taking into account the spatial inhomogeneity of limiting magnitudes. After removing spurious sources by careful masking and visual inspection of coadded and multi-epoch images obtained over the 7 yr of the surveys, we construct LAE samples consisting of 6995, 4641, 726, 6124, 2058, 18, and 5 LAEs at $z=2.2$, 3.3, 4.9, 5.7, 6.6, 7.0, and 7.3, respectively, although the $z=7.3$ candidates are tentative. Our LAE catalogs contain 241 spectroscopically confirmed LAEs at the expected redshifts from previous work. We demonstrate that the number counts of our LAEs are consistent with previous studies with similar LAE selection criteria. The LAE catalogs will be made public on our project webpage with detailed descriptions of the content and ancillary information about the masks and limiting magnitudes., Comment: 38 pages, 19 Figures, 5 Tables. Accepted for publication in ApJS

Audio classification and restoration are among major downstream tasks in audio signal processing. However, restoration derives less of a benefit from pretrained models compared to the overwhelming success of pretrained models in classification tasks. Due to such unbalanced benefits, there has been rising interest in how to improve the performance of pretrained models for restoration tasks, e.g., speech enhancement (SE). Previous works have shown that the features extracted by pretrained audio encoders are effective for SE tasks, but these speech-specialized encoder-only models usually require extra decoders to become compatible with SE, and involve complicated pretraining procedures or complex data augmentation. Therefore, in pursuit of a universal audio model, the audio masked autoencoder (MAE) whose backbone is the autoencoder of Vision Transformers (ViT-AE), is extended from audio classification to SE, a representative restoration task with well-established evaluation standards. ViT-AE learns to restore masked audio signal via a mel-to-mel mapping during pretraining, which is similar to restoration tasks like SE. We propose variations of ViT-AE for a better SE performance, where the mel-to-mel variations yield high scores in non-intrusive metrics and the STFT-oriented variation is effective at intrusive metrics such as PESQ. Different variations can be used in accordance with the scenarios. Comprehensive evaluations reveal that MAE pretraining is beneficial to SE tasks and help the ViT-AE to better generalize to out-of-domain distortions. We further found that large-scale noisy data of general audio sources, rather than clean speech, is sufficiently effective for pretraining., Comment: WASPAA 2023.Copyright 2023 IEEE.Personal use of this material is permitted.Permission from IEEE must be obtained for all other uses,in any current or future media,including reprinting/republishing this material for advertising or promotional purposes, creating new collective works,for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works

We report a search for lepton-flavor-violating decays $\tau^- \to \ell^- \phi$ ($\ell^- =e^-,\mu^-$) at the Belle II experiment, using a sample of electron-positron data produced at the SuperKEKB collider in 2019-2021 and corresponding to an integrated luminosity of 190 fb$^{-1}$. We use a new untagged selection for $e^+e^- \to \tau^+\tau^-$ events, where the signal $\tau$ is searched for as a neutrinoless final state of a single charged lepton and a $\phi$ meson and the other $\tau$ is not reconstructed in any specific decay mode, in contrast to previous measurements by the BaBar and Belle experiments. We find no evidence for $\tau^- \to \ell^- \phi$ decays and obtain upper limits on the branching fractions at 90% confidence level of 23 $\times 10^{-8}$ and 9.7$\times 10^{-8}$ for $\tau^- \rightarrow e^-\phi$ and $\tau^- \rightarrow \mu^-\phi$, respectively