Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition

Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current video diffusion models often attempt to process high-dimensional videos directly. To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation. Specifically, we propose an autoencoder that succinctly encodes a video as a combination of a content frame (like an image) and a low-dimensional motion latent representation. The former represents the common content, and the latter represents the underlying motion in the video, respectively. We generate the content frame by fine-tuning a pretrained image diffusion model, and we generate the motion latent representation by training a new lightweight diffusion model. A key innovation here is the design of a compact latent space that can directly utilizes a pretrained image diffusion model, which has not been done in previous latent video diffusion models. This leads to considerably better quality generation and reduced computational costs. For instance, CMD can sample a video 7.7$\times$ faster than prior approaches by generating a video of 512$\times$1024 resolution and length 16 in 3.1 seconds. Moreover, CMD achieves an FVD score of 212.7 on WebVid-10M, 27.3% better than the previous state-of-the-art of 292.4.
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling

Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy.Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification. In particular, we achieve state-of-the-art semantic segmentation results of 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset
Spectro-polarimetric study to constrain accretion-ejection properties of MCG-5-23-16 using IXPE and NuSTAR observations

We conducted a study on the X-ray polarization properties of MGC-5-23-16 by analyzing long-term monitoring data from {\it NuSTAR} jointly with {\it IXPE} observations in May and November 2022. The re-analysis of {\it IXPE} data gives model-dependent polarization degree, PD (\%) = $1.55\pm0.99$ and $1.31\pm0.95$ in the energy band $2-8$ keV, agrees with previous studies within error bars. The model-independent analysis of PD poses an upper limit of $\leq3.8$ ($1\sigma$ level) for the same energy band. The observed upper limit of PD, along with broadband spectral analysis ($3-79$ keV), allowed us to derive corona geometry (i.e. length and height) and the accretion disk inclination ($\sim 33^\circ$). Additional {\it NuSTAR} observations were also analyzed to gain insights into the accretion flow properties of the source and to estimate the expected polarization during those epochs with PD $\sim 4.3\%$. The radius and height of the corona exhibited to vary between $28.2\pm3.1 - 39.8\pm4.6$ r$_s$ and $14.3\pm1.7-21.4\pm1.9$ r$_s$, with a mass outflow rate from the corona measuring $0.14\pm0.03-0.2\pm0.03$ Eddington rate ($\dot M_{\rm Edd}$). The estimated PD values were nearly constant up to a certain radial distance and height of the corona and then decreased for increasing corona geometry. The spectral analysis further provided an estimate for the mass of the central black hole $\sim 2\times 10^7$ M$_\odot$ and the velocity of the outflowing gas $\sim 0.16-0.19c$. Our modeling of the disk-corona-outflows and polarization connection can be extended and validated with data from recently launched India's first X-ray Polarimeter Satellite, offering potential applications to other sources.
Effects of vitamin C and α-lipoid acid dietary supplementations on metabolic adaptation of broilers to heat stress

SUMMARY The objective of this study was to investigate the effects of vitamin C and α-lipoic acid dietary supplementations for 42 days on metabolic response of broilers to heat stress during entire feeding period. Whereas negative control birds were kept to 24°C as ambient temperature, heat stressed broilers fed with basal diets (stress positive controls) and supplemented with vitamin C (250 mg/kg/day) or with a-lipoic acid (250 mg/kg/day) were exposed to 34°C for 8 hours every day. Serum biochemical markers, serum lipid profiles, blood gas analysis and acid-base regulation were assessed at the end of the experimental period. Despite the long term heat exposure, serum concentrations of glucose, total proteins, albumin, globulins, lipids (triglycerides, cholesterol and lipoproteins), electrolytes (Na + , K + and Cl ) and iron as well as serum liver enzyme (AST, ALT, GGT and ALP) activities were not significantly altered compared to the negative controls. By contrast, pancreatic amylase activity was dramatically increased, the uric acid and Mg concentrations were significantly declined and Ca and P concentrations also tended to decrease in birds exposed to heat stress. In parallel, the respiratory rate was markedly accelerated leading to amplification in blood gas exchanges (significant increase in pO 2 , weak decrease in pCO 2 and in HCO 3 concentrations, weak increase in blood pH) and to significant reduction in haemoglobinemia. In addition, a-lipoic acid supplementation has significantly changed serum lipid profiles by increasing the triacylglycerol proportions and decreasing the free fatty acid and polar lipid percentages. Dietary supplementations with vitamin C and mainly with a-lipoic acid have not significantly modified blood gas exchanges and acid-base equilibrium but have partly corrected the metabolic response.
Modelling the pulse transformer in SPICE

The paper is devoted to modelling pulse transformers in SPICE. It shows the character of the selected models of this element, points out their advantages and disadvantages, and presents the results of experimental verification of the considered models. These models are characterized by varying degrees of complexity - from linearly coupled linear coils to nonlinear electrothermal models. The study was conducted for transformer with ring cores made of a variety of ferromagnetic materials, while exciting the sinusoidal signal of a frequency 100 kHz and different values of load resistance. The transformers operating conditions under which the considered models ensure the acceptable accuracy of calculations are indicated.
Collecting Direct Non-Health Care and Time Cost Data: Application to Screening and Diagnosis of Cervical Cancer

Background . Data on direct non-health care and time costs are rarely collected, though the incorporation of such data is essential for performing cost-effectiveness analyses according to established guidelines. Objectives . To explore the challenges involved in collecting and analyzing these data from patients enrolled in a clinical trial. Methods . Through the use of a pilot study, the authors designed a questionnaire to collect these costs. They used this questionnaire in a clinical trial conducted at a comprehensive cancer center and a public community hospital. Patients in the trial were undergoing screening or diagnostic procedures through a clinical protocol designed to measure the effectiveness of fluorescence and reflectance spectroscopy for detecting cervical precancers. Direct non-health care costs were adjusted to 2003 constant dollars. Results . The authors successfully collected direct non-health care and time cost data, thus demonstrating the feasibility of acquiring such data. Compared to patients receiving diagnostic services for cervical cancer, those receiving screening services for the same condition in both settings incurred lower direct non-health care costs and time costs, as defined in the questionnaire. Compared to patients receiving either service at the comprehensive cancer center, those seeking either service at the public community hospital incurred lower direct non-health care costs and time costs. When outliers were removed, total direct non-health care costs and time costs substantially decreased for diagnostic patients in the comprehensive cancer center; total direct non-health care costs and time costs for other subgroups remained essentially unchanged. Conclusions . Direct non-health care and time cost data can be collected within a large-scale clinical trial. The setting (community v. specialty hospital) and population (patients receiving screening v. diagnostic examination) makes a difference regarding the cost totals. The order of magnitude of the final result depends on the context in which the non-health care and time cost data will be used.
Antibacterial action of Chlorhexidine/thymol containing varnishes in vitro and in vivo.

OBJECTIVES The antibacterial activity of two different formulations of a chlorhexidine/thymol varnish should be elucidated in vitro and in vivo. METHODS The agar diffusion assay with Cervitec(®) and CervitecPlus(®) and three reference strains each of streptococci, lactobacilli, actinomyces and periodontal pathogens was performed. In a split-mouth study, 40 volunteers applied the test (CervitecPlus(®), solvent water and ethanol) and control (Cervitec(®), solvent ethyl acetate) varnish at buccal recessions of premolar teeth at baseline as well as after two, four and seven days. Supra- and subgingival plaques were collected 2 weeks before baseline and at the screening appointments. Supragingival plaque was analysed for mutans streptococci and lactobacilli and subgingival samples for Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, Porphyromonas gingivalis and Porphyromonas intermedia. Friedman/Wilcoxon tests and U-test were used for statistical analysis (P < 0.05). RESULTS Most reference strains were susceptible with inhibition zones (mm) as follows: Cervitec(®)/CervitecPlus(®) streptococci 27 ± 1.7/21.3 ± 2.5, lactobacilli 26 ± 9.2/23.7 ± 4.9, actinomyces 36.3 ± 6.6/27.3 ± 1.5, periodontal pathogens 18.7 ± 7.6/18 ± 1.7. Both varnishes reduced significantly the counts of mutans streptococci and lactobacilli in the patients. However, no significant differences were found between test and control sides at any time. The total counts of periodontal pathogens were low. A tendency to higher counts of A. actinomycetemcomitans at the control side could be shown; the test side did not harbour significantly higher counts. CONCLUSION Both varnishes may influence the plaque formation and reduce mutans streptococci in supragingival plaque.
Geometric Bounds for Persistence

In this paper, we bring a new perspective to persistent homology by incorporating key concepts from metric geometry. For a given compact subset $X$ of a Banach space $Y$, we study the topological features appearing in family $N_\bullet(X\subset Y)$ of nested of neighborhoods of $X$ in $Y$ and give several geometric bounds on their persistence (lifespans). First, we discuss the lifespans of these homology classes in terms of their filling radii in $Y$. By using this, we relate these lifespans to key invariants in metric geometry such as Urysohn width. Next, we bound these lifespans via $\ell^\infty$-principal components of the set $X$, which are also known as Kolmogorov widths. Furthermore, we introduce and study the notion of extinction time of a space $X$: the critical threshold after which there are no homological features alive in any degree. We also describe novel approaches on how to estimate \v{C}ech and Vietoris-Rips extinction times of a set $X$ by relating $X$ to its \"uber-contractible cores and to its tight span, respectively.
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction

Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this paper, we introduce the Spatial-Temporal Graph Encoder-Decoder (STGED) framework for Tactical Communication Networks that leverages both spatial and temporal features of network states to learn latent tactical behaviors effectively. STGED hierarchically utilizes graph-based attention mechanism to spatially encode a series of communication network states, leverages a recurrent neural network to temporally encode the evolution of states, and a fully-connected feed-forward network to decode the connectivity in the future state. Through extensive experiments, we demonstrate that STGED consistently outperforms baseline models by large margins across different time-steps input, achieving an accuracy of up to 99.2\% for the future state prediction task of tactical communication networks.
An analysis of the relationship between signal-derived vocal arousal score and human emotion production and perception

Bone et al. recently proposed an unsupervised signal-derived vocal arousal score (VC-AS) based on fusion of three intuitive acoustic features, i.e., pitch, intensity, and HF500, and have shown the effectiveness of quantifying humans perceptual ratings of arousal robustly across multiple corpora. Due to the readily-applicable nature of the system, this objective quantification scheme could foresee-ably be used in multiple fields of behavioral science as an objective measure of affect. In this work, we investigate in detail the relationship of this signalderived measure to both intended arousal expression (i.e., production aspect) and perceived arousal rating (i.e., perception aspect). On the perception side, our results in three databases (EMA, VAM, and IEMOCAP) indicate that VC-AS agrees with mean perception at least as well as an average individual rater does. Regarding production, we demonstrate that intended arousal correlates more with VC-AS than mean perception (EMA and IEMOCAP). We also show that VC-AS correlates more with intended arousal than perceived arousal (EMA); this finding is quite surprising given that the framework is supported by extensive affective perception studies, although there is physiological motivation as well. Implication for utilizing VC-AS for novel scientific study in engineering (e.g., to mitigate subjectivity) is further discussed.
Dynamical importance and network perturbations

The leading eigenvalue $\lambda$ of the adjacency matrix of a graph exerts much influence on the behavior of dynamical processes on that graph. It is thus relevant to relate notions of the importance (specifically, centrality measures) of network structures to $\lambda$ and its associated eigenvector. We study a previously derived measure of edge importance known as"dynamical importance", which estimates how much $\lambda$ changes when one removes an edge from a graph or adds an edge to it. We examine the accuracy of this estimate for different network structures and compare it to the true change in $\lambda$ after an edge removal or edge addition. We then derive a first-order approximation of the change in the leading eigenvector. We also consider the effects of edge additions on Kuramoto dynamics on networks, and we express the Kuramoto order parameter in terms of dynamical importance. Through our analysis and computational experiments, we find that studying dynamical importance can improve understanding of the relationship between network perturbations and dynamical processes on networks.
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models

Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fr\'echet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet.
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training

Unsupervised intrinsic image decomposition (IID) is the process of separating a natural image into albedo and shade without these ground truths. A recent model employing light detection and ranging (LiDAR) intensity demonstrated impressive performance, though the necessity of LiDAR intensity during inference restricts its practicality. Thus, IID models employing only a single image during inference while keeping as high IID quality as the one with an image plus LiDAR intensity are highly desired. To address this challenge, we propose a novel approach that utilizes only an image during inference while utilizing an image and LiDAR intensity during training. Specifically, we introduce a partially-shared model that accepts an image and LiDAR intensity individually using a different specific encoder but processes them together in specific components to learn shared representations. In addition, to enhance IID quality, we propose albedo-alignment loss and image-LiDAR conversion (ILC) paths. Albedo-alignment loss aligns the gray-scale albedo from an image to that inferred from LiDAR intensity, thereby reducing cast shadows in albedo from an image due to the absence of cast shadows in LiDAR intensity. Furthermore, to translate the input image into albedo and shade style while keeping the image contents, the input image is separated into style code and content code by encoders. The ILC path mutually translates the image and LiDAR intensity, which share content but differ in style, contributing to the distinct differentiation of style from content. Consequently, LIET achieves comparable IID quality to the existing model with LiDAR intensity, while utilizing only an image without LiDAR intensity during inference.
Needle in a haystack: efficiently finding atomically defined quantum dots for electrostatic force microscopy

The ongoing development of single electron, nano and atomic scale semiconductor devices would benefit greatly from a characterization tool capable of detecting single electron charging events with high spatial resolution, at low temperature. In this work, we introduce a novel Atomic Force Microscope (AFM) instrument capable of measuring critical device dimensions, surface roughness, electrical surface potential, and ultimately the energy levels of quantum dots and single electron transistors in ultra miniaturized semiconductor devices. Characterization of nanofabricated devices with this type of instrument presents a challenge: finding the device. We therefore also present a process to efficiently find a nanometre size quantum dot buried in a $10 \times 10~\text{mm}^2$ silicon sample using a combination of optical positioning, capacitive sensors and AFM topography in vacuum.
Jointly benchmarking small and structural variant calls with vcfdist. Author

Recent improvements in long-read sequencing accuracy have enabled calling phased small and structural variants from a single analysis pipeline. Despite this, the current standard tools for variant calling evaluation are only designed for either small (vcfeval) or large (Truvari) variants. In this work we extend vcfdist – previously a small variant calling evaluator – to evaluate structural variants, making it the first benchmarking tool to jointly evaluate phased single-nucleotide polymorphisms (SNPs), small insertions/deletions (INDELs), and structural variants (SVs) for the whole genome. We find that a joint evaluation reduces measured false negative and false positive variant calls across the board: by 28.1% for SNPs, 19.1% for INDELs, and 52.4% for SVs over 50 bases. Building on vcfdist’s alignment-based evaluation, we also jointly analyze phasing accuracy. vcfdist identifies that 43% to 92% of all flip errors called by standard phasing evaluation tool WhatsHap are false positives due to differences in variant representations.
Discovery of Eremiobacterota with nifH homologs in tundra soil

We describe the genome of an Eremiobacterota population from tundra soil that contains the minimal set of nif genes needed to fix atmospheric N2. This putative diazotroph population, which we name Candidatus Lamibacter sapmiensis, links for the first time Eremiobacterota and N2 fixation. The integrity of the genome and its nif genes are well supported by both environmental and taxonomic signals. Ca. Lamibacter sapmiensis contains three nifH homologs and the complementary set of nifDKENB genes that are needed to assemble a functional nitrogenase. The putative diazotrophic role of Ca. Lamibacter sapmiensis is supported by the presence of genes that regulate N2 fixation and other genes involved in downstream processes such as ammonia assimilation. Similar to other Eremiobacterota, Ca. Lamibacter sapmiensis encodes the potential for atmospheric chemosynthesis via CO2 fixation coupled with H2 and CO oxidation. Interestingly, the presence of a N2O reductase indicates that this population could play a role as a N2O sink in tundra soils. Due to the lack of activity data, it remains uncertain if Ca. Lamibacter sapmiensis is able to assemble a functional nitrogenase and participate in N2 fixation. Confirmation of this ability would be a testament to the great metabolic versatility of Eremiobacterota, which appears to underlie their ecological success in cold and oligotrophic environments.