Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about 1%-area edits, SIGE accelerates DDPM by <inline-formula><tex-math notation="LaTeX">$3.0\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>. </mml:mo><mml:mn>0</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq1-3316020.gif"/></alternatives></inline-formula> on NVIDIA RTX 3090 and <inline-formula><tex-math notation="LaTeX">$4.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>4</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq2-3316020.gif"/></alternatives></inline-formula> on Apple M1 Pro GPU, Stable Diffusion by <inline-formula><tex-math notation="LaTeX">$7.2\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>7</mml:mn><mml:mo>. </mml:mo><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq3-3316020.gif"/></alternatives></inline-formula> on 3090, and GauGAN by <inline-formula><tex-math notation="LaTeX">$5.6\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>5</mml:mn><mml:mo>. </mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq4-3316020.gif"/></alternatives></inline-formula> on 3090 and <inline-formula><tex-math notation="LaTeX">$5.2\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>5</mml:mn><mml:mo>. </mml:mo><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq5-3316020.gif"/></alternatives></inline-formula> on M1 Pro GPU. Compared to our conference paper, we enhance SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results to substantiate the efficacy of our method.
Primeval very low-mass stars and brown dwarfs – VI. Population properties of metal-poor degenerate brown dwarfs

We presented 15 new T dwarfs that were selected from UKIRT Infrared Deep Sky Survey, Visible and Infrared Survey Telescope for Astronomy , and Wide-field Infrared Survey Explorer surveys, and confirmed with optical to near-infrared spectra obtained with the Very Large Telescope and the Gran Telescopio Canarias. One of these new T dwarfs is mildly metal-poor with slightly suppressed K-band flux. We presented a new X-shooter spectrum of a known benchmark sdT5.5 subdwarf, HIP 73786B. To better understand observational properties of brown dwarfs, we discussed transition zones (mass ranges) with low-rate hydrogen, lithium, and deuterium burning in brown dwarf population. The hydrogen burning transition zone is also the substellar transition zone that separates very low-mass stars, transitional, and degenerate brown dwarfs. Transitional brown dwarfs have been discussed in previous works of the Primeval series. Degenerate brown dwarfs without hydrogen fusion are the majority of brown dwarfs. Metal-poor degenerate brown dwarfs of the Galactic thick disc and halo have become T5+ subdwarfs. We selected 41 T5+ subdwarfs from the literature by their suppressed K-band flux. We studied the spectral-type–colour correlations, spectral-type–absolute magnitude correlations, colour–colour plots, and HR diagrams of T5+ subdwarfs, in comparison to these of L–T dwarfs and L subdwarfs. We discussed the T5+ subdwarf discovery capability of deep sky surveys in the 2020s.
Latent Prompt Tuning for Text Summarization

Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties of generated summaries and potentially improve summarization quality (since more information are given). Unfortunately, control signals are not already available during inference time. In this paper, we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled (without control signals) modes. During training, Lotus learns latent prompt representations from prompts with gold control signals using a contrastive learning objective. Experiments show Lotus in uncontrolled mode consistently improves upon strong (uncontrollable) summarization models across four different summarization datasets. We also demonstrate generated summaries can be controlled using prompts with user specified control tokens.
CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation

Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists. Recent advancements in Radiology Report Generation (RRG) are largely driven by improving a model's capabilities in encoding single-modal feature representations, while few studies explicitly explore the cross-modal alignment between image regions and words. Radiologists typically focus first on abnormal image regions before composing the corresponding text descriptions, thus cross-modal alignment is of great importance to learn a RRG model which is aware of abnormalities in the image. Motivated by this, we propose a Class Activation Map guided Attention Network (CAMANet) which explicitly promotes cross-modal alignment by employing aggregated class activation maps to supervise cross-modal attention learning, and simultaneously enrich the discriminative information. Experimental results demonstrate that CAMANet outperforms previous SOTA methods on two commonly used RRG benchmarks.
ATCA search for 21 cm emission from a candidate damped Ly-$\alpha$ absorber at ${\vec z} = 0.101$

We report a deep search for 21 cm emission/absorption from the $z \sim 0.101$ candidate damped Lyman- α system towards PKS 0439-433, using the Australia Telescope Compact Array (ATCA). The spectrum shows a weak absorption feature -at the $3.3 \sigma$ level -which yields a lower limit of 730 K on the spin temperature of the system. No H i emission was detected: the $3\sigma$ upper limit on the H i mass of the absorber is 2.25 10$^9 M_{\odot}$, for a velocity spread of ∼ 70 km s -1 . The low H i mass and the high spin temperature seem to rule out the possibility that the absorber is a large gas-rich spiral galaxy.
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence

Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of learning algorithms for classical games into the quantum realm. In this paper, we focus on learning in quantum zero-sum games under Matrix Multiplicative Weights Update (a generalization of the multiplicative weights update method) and its continuous analogue, Quantum Replicator Dynamics. When each player selects their state according to quantum replicator dynamics, we show that the system exhibits conservation laws in a quantum-information theoretic sense. Moreover, we show that the system exhibits Poincare recurrence, meaning that almost all orbits return arbitrarily close to their initial conditions infinitely often. Our analysis generalizes previous results in the case of classical games.
SPIRAL-INDUCED STAR FORMATION IN THE OUTER DISKS OF GALAXIES

The outer regions of galactic disks have received increased attention since ultraviolet observations with Galaxy Evolution Explorer demonstrated that nearly 30% of galaxies have UV emission beyond their optical extents, indicating star formation activity. These galaxies have been termed extended UV (XUV) disks. Here, we address whether these observations contradict the gas surface density threshold for star formation inferred from radial profiles of galaxies. We run smoothed particle hydrodynamic simulations of isolated disk galaxies with fiducial star formation prescriptions and show that over-densities owing to the presence of spiral structure can induce star formation in extended gas disks. For direct comparison with observations, we use the three-dimensional radiative transfer code Sunrise to create simulated FUV and Ks-band images. We find that galaxies classified as Type I XUV disks are a natural consequence of spiral patterns, but we are unable to reproduce Type II XUV disks. We also compare our results to studies of the Kennicutt–Schmidt relation in outer disks.
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the present article, we aim to quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes of~\carboneq~if we consider only the dynamic power consumption, and 50.5 tonnes if we account for all processes ranging from equipment manufacturing to energy-based operational consumption. We also study the energy requirements and carbon emissions of its deployment for inference via an API endpoint receiving user queries in real-time. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of ML models and future research directions that can contribute towards improving carbon emissions reporting.
When do contrastive learning signals help spatio-temporal graph forecasting?

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements. Meanwhile, contrastive learning has been an effective method for providing self-supervision signals and addressing data scarcity in various domains. In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting? To answer this question, we present the first systematic exploration on incorporating contrastive learning into STG forecasting. Specifically, we first elaborate two potential schemes for integrating contrastive learning. We then propose two feasible and efficient designs of contrastive tasks that are performed on the node or graph level. The empirical study on STG benchmarks demonstrates that integrating graph-level contrast with the joint learning scheme achieves the best performance. In addition, we introduce four augmentations for STG data, which perturb the data in terms of graph structure, time domain, and frequency domain. Experimental results reveal that the model is not sensitive to the proposed augmentations' semantics. Lastly, we extend the classic contrastive loss via a rule-based strategy that filters out the most semantically similar negatives, yielding performance gains. We also provide explanations and insights based on the above experimental findings. Code is available at https://github.com/liuxu77/STGCL.
All the Codeword Symbols in Polar Codes Have the Same SER Under the SC Decoder

Let <inline-formula> <tex-math notation="LaTeX">${\mathbb F}_{p}$ </tex-math></inline-formula> be a prime field and let <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> be a larger finite field obtained from adjoining an element <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">${\mathbb F}_{p}$ </tex-math></inline-formula>, i.e., <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}= {\mathbb F}_{p}(\alpha)$ </tex-math></inline-formula>. We consider polar codes constructed from the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> kernel <inline-formula> <tex-math notation="LaTeX">$\begin{aligned} \begin{bmatrix} 1 & 0 \\ \alpha & 1 \end{bmatrix} \end{aligned}$ </tex-math></inline-formula> over <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula>. We prove that for any <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula>-symmetric memoryless channel, any code length, and any code dimension, all the codeword symbols in such polar codes have the same symbol error rate (SER) under the successive cancellation (SC) decoder.
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks

The size of deep neural networks has grown exponentially in recent years. Unfortunately, hardware devices have not kept pace with the rapidly increasing memory requirements. To cope with this, researchers have turned to techniques such as spilling and recomputation, which increase training time, or reduced precision and model pruning, which can affect model accuracy. We present OLLA, an algorithm that optimizes the lifetime and memory location of the tensors used to train neural networks. Our method reduces the memory usage of existing neural networks, without needing any modification to the models or their training procedures. We formulate the problem as a joint integer linear program (ILP). We present several techniques to simplify the encoding of the problem, and enable our approach to scale to the size of state-of-the-art neural networks using an off-the-shelf ILP solver. We experimentally demonstrate that OLLA only takes minutes if not seconds to allow the training of neural networks using one-third less memory on average.
The Mass Spectrum of Metal-free Stars Resulting from Photodissociation Feedback: A Scenario for the Formation of Low-Mass Population III Stars

The initial mass function (IMF) of metal-free stars that form in the initial starburst of massive (virial temperatures ≳104 K) metal-free protogalaxies is studied. In particular, we focus on the effect of H2 photodissociation by preexisting stars on the fragmentation mass scale, presumedly determined by the Jeans mass at the end of the initial free-fall phase, i.e., at the so-called loitering phase, characterized by the temporary temperature minimum. Photodissociation diminishes the Jeans mass at the loitering phase, thereby reducing the fragmentation mass scale of primordial clouds. Thus, in a given cloud, far-ultraviolet (FUV) radiation from the first star, which is supposedly very massive (~103 M☉), reduces the mass scale for subsequent fragmentation. Through a series of similar processes the IMF for metal-free stars is established. If FUV radiation exceeds a threshold level, the star-forming clumps collapse solely through atomic cooling. Correspondingly, the fragmentation scale drops discontinuously from a few × 10 M☉ to subsolar scales. In compact clouds (≲1.6 kpc for clouds of gas mass 108 M☉), this level of radiation field is attained and subsolar-mass stars are formed, even in a metal-free environment. Consequently, the IMF becomes bimodal, with peaks at a few tenths M☉ and a few × 10 M☉. The high-mass portion of the IMF, ξhigh(m*), is found to be a very steep function of the stellar mass m*, ξhigh(m*) ∝ m. Therefore, the typical mass scale of metal-free stars is significantly smaller than that of the very first stars. In an appendix we study the thermal instability in collapsing primordial prestellar cores and discuss why the thermal instability occurring during the three-body H2 formation does not appear to manifest itself in causing further fragmentation of such cores.
The Updated Multiple Star Catalog

The catalog of hierarchical stellar systems with three or more components is an update of the original 1997 version. For 2000 hierarchies, the new Multiple Star Catalog (MSC) provides distances, component masses and periods, and supplementary information (astrometry, photometry, identifiers, orbits, notes). The MSC content and format are explained, and its incompleteness and strong observational selection are stressed. Nevertheless, the MSC can be used for statistical studies and is a valuable source for planning observations of multiple stars. Rare classes of stellar hierarchies found in the MSC (with six or seven components, extremely eccentric orbits, planar and possibly resonant orbits, hosting planets) are briefly presented. High-order hierarchies have smaller velocity dispersion compared to triples and are often associated with moving groups. The paper concludes with an analysis of the ratio of periods and separations between inner and outer subsystems. In wide hierarchies, the ratio of semimajor axes, estimated statistically, is distributed between 3 and 300, with no evidence of dynamically unstable systems.
Key research questions for implementation of artificial intelligence in capsule endoscopy

Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
Health Messaging During a Pandemic: How Information Type and Individual Factors Influence Responses to COVID-19 Messages

Traditional approaches to public health messaging suggest successful COVID-19 messages should communicate about the health threat and present effective protective behaviors. However, as the pandemic continues, how individual factors affect audience responses to such messages needs to be explored. We surveyed 224 U.S. residents (equal distribution among age group, education level, and gender) in a 2 (health threat information: high versus low) × 2 (self-efficacy information: present versus absent) × 2 (response efficacy information: present versus absent) experimental design. Variations in message information did not influence mask wearing and handwashing behavioral intentions. Instead, participant responses followed reactance theory predictions. Feelings of fear about COVID-19 and reactance proneness predicted a perceived freedom threat. Perceiving a freedom threat predicted reactance to the COVID-19 message, which was associated with decreased intentions to wear a mask and handwash. Political ideology was also associated with behavioral intentions. The more conservative a person identified, the less likely they were to intend to engage in COVID-19 protection behaviors. Our findings call into question the effectiveness of traditional health messaging during a pandemic and demonstrate the implications of politicizing health behaviors.
AsgeneDB: a curated orthology arsenic metabolism gene database and computational tool for metagenome annotation

Abstract Arsenic (As) is the most ubiquitous toxic metalloid in nature. Microbe-mediated As metabolism plays an important role in global As biogeochemical processes, greatly changing its toxicity and bioavailability. While metagenomic sequencing may advance our understanding of the As metabolism capacity of microbial communities in different environments, accurate metagenomic profiling of As metabolism remains challenging due to low coverage and inaccurate definitions of As metabolism gene families in public orthology databases. Here we developed a manually curated As metabolism gene database (AsgeneDB) comprising 400 242 representative sequences from 59 As metabolism gene families, which are affiliated with 1653 microbial genera from 46 phyla. AsgeneDB achieved 100% annotation sensitivity and 99.96% annotation accuracy for an artificial gene dataset. We then applied AsgeneDB for functional and taxonomic profiling of As metabolism in metagenomes from various habitats (freshwater, hot spring, marine sediment and soil). The results showed that AsgeneDB substantially improved the mapping ratio of short reads in metagenomes from various environments. Compared with other databases, AsgeneDB provides more accurate, more comprehensive and faster analysis of As metabolic genes. In addition, we developed an R package, Asgene, to facilitate the analysis of metagenome sequencing data. Therefore, AsgeneDB and the associated Asgene package will greatly promote the study of As metabolism in microbial communities in various environments.
Impact of different oral treatments on the composition of the supragingival plaque microbiome

ABSTRACT Background Antiseptics are used to inhibit oral biofilm growth. However, they affect not only pathogenic but also commensal bacteria, which are a natural barrier against oral diseases. Objective Using a metaproteome approach combined with a standard plaque-regrowth study, this pilot study examined the impact of different concentrations of lactoperoxidase (LPO)-system containing lozenges on early plaque formation, and active biological processes. Design Sixteen orally healthy subjects received four local treatments as a randomized single-blind study based on a cross-over design. Two lozenges containing components of the LPO-system in different concentrations were compared to a placebo and Listerine®. The newly formed dental plaque was analyzed by mass spectrometry (nLC-MS/MS). Results On average 1,916 metaproteins per sample were identified, which could be assigned to 116 genera and 1,316 protein functions. Listerine® reduced the number of metaprotein groups and their relative abundance, confirming the plaque inhibiting effect. The LPO-lozenges triggered mainly higher metaprotein abundances of early and secondary colonizers as well as bacteria associated with dental health but also periodontitis. Functional information indicated plaque biofilm growth. Conclusion The effects of Listerine® and LPO-system containing lozenges used for plaque inhibition are different. In contrast to Listerine®, the lozenges allowed maintenance of a higher bacterial diversity.