Miracle-dz/UniFAR-FTUs huggingface dataset:
ColorizeDiffusion: Adjustable Sketch Colorization with Reference Image and Text

Recently, diffusion models have demonstrated their effectiveness in generating extremely high-quality images and have found wide-ranging applications, including automatic sketch colorization. However, most existing models use text to guide the conditional generation, with fewer attempts exploring the potential advantages of using image tokens as conditional inputs for networks. As such, this paper exhaustively investigates image-guided models, specifically targeting reference-based sketch colorization, which aims to colorize sketch images using reference color images. We investigate three critical aspects of reference-based diffusion models: the shortcomings compared to text-based counterparts, the training strategies, and the capability in zero-shot, sequential text-based manipulation. We introduce two variations of an image-guided latent diffusion model using different image tokens from the pre-trained CLIP image encoder, and we propose corresponding manipulation methods to adjust their results sequentially using weighted text inputs. We conduct comprehensive evaluations of our models through qualitative and quantitative experiments, as well as a user study.
Navigating Uncertainty: Optimizing API Dependency for Hallucination Reduction in Closed-Book Question Answering

While Large Language Models (LLM) are able to accumulate and restore knowledge, they are still prone to hallucination. Especially when faced with factual questions, LLM cannot only rely on knowledge stored in parameters to guarantee truthful and correct answers. Augmenting these models with the ability to search on external information sources, such as the web, is a promising approach to ground knowledge to retrieve information. However, searching in a large collection of documents introduces additional computational/time costs. An optimal behavior would be to query external resources only when the LLM is not confident about answers. In this paper, we propose a new LLM able to self-estimate if it is able to answer directly or needs to request an external tool. We investigate a supervised approach by introducing a hallucination masking mechanism in which labels are generated using a close book question-answering task. In addition, we propose to leverage parameter-efficient fine-tuning techniques to train our model on a small amount of data. Our model directly provides answers for $78.2\%$ of the known queries and opts to search for $77.2\%$ of the unknown ones. This results in the API being utilized only $62\%$ of the time.
S2-DMs: Skip-Step Diffusion Models

Diffusion models have emerged as powerful generative tools, rivaling GANs in sample quality and mirroring the likelihood scores of autoregressive models. A subset of these models, exemplified by DDIMs, exhibit an inherent asymmetry: they are trained over $T$ steps but only sample from a subset of $T$ during generation. This selective sampling approach, though optimized for speed, inadvertently misses out on vital information from the unsampled steps, leading to potential compromises in sample quality. To address this issue, we present the S$^{2}$-DMs, which is a new training method by using an innovative $L_{skip}$, meticulously designed to reintegrate the information omitted during the selective sampling phase. The benefits of this approach are manifold: it notably enhances sample quality, is exceptionally simple to implement, requires minimal code modifications, and is flexible enough to be compatible with various sampling algorithms. On the CIFAR10 dataset, models trained using our algorithm showed an improvement of 3.27% to 14.06% over models trained with traditional methods across various sampling algorithms (DDIMs, PNDMs, DEIS) and different numbers of sampling steps (10, 20, ., 1000). On the CELEBA dataset, the improvement ranged from 8.97% to 27.08%. Access to the code and additional resources is provided in the github.
Reconstructing almost all of a point set in $\mathbb{R}^d$ from randomly revealed pairwise distances

Let $V$ be a set of $n$ points in $\mathbb{R}^d$, and suppose that the distance between each pair of points is revealed independently with probability $p$. We study when this information is sufficient to reconstruct large subsets of $V$, up to isometry. Strong results for $d=1$ have been obtained by Gir\~ao, Illingworth, Michel, Powierski, and Scott. In this paper, we investigate higher dimensions, and show that if $p>n^{-2/(d+4)}$, then we can reconstruct almost all of $V$ up to isometry, with high probability. We do this by relating it to a polluted graph bootstrap percolation result, for which we adapt the methods of Balogh, Bollob\'as, and Morris.
Specific Emitter Identification Based on Joint Variational Mode Decomposition

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, the trade-off of VMD between the robustness to noise and the ability to preserve signal information has not been investigated in the current literature. Moreover, the existing VMD algorithm does not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, consequently constraining its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters and reduces noise. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with VMD, the JVMD algorithm improves the accuracy of device classification and the robustness towards noise.
NAD+ precursors and bile acid sequestration treat preclinical refractory environmental enteric dysfunction

Environmental enteric dysfunction (EED) is a diffuse small bowel disorder associated with poor growth, inadequate responses to oral vaccines, and nutrient malabsorption in millions of children worldwide. We identify loss of the small intestinal Paneth and goblet cells that are critical for innate immunity, reduced villous height, increased bile acids, and dysregulated nicotinamide adenine dinucleotide (NAD+) synthesis signaling as potential mechanisms underlying EED and which also correlated with diminished length-for-age z score. Isocaloric low-protein diet (LPD) consumption in mice recapitulated EED histopathology and transcriptomic changes in a microbiota-independent manner, as well as increases in serum and fecal bile acids. Children with refractory EED harbor single-nucleotide polymorphisms in key enzymes involved in NAD+ synthesis. In mice, deletion of Nampt, the gene encoding the rate-limiting enzyme in the NAD+ salvage pathway, from intestinal epithelium also reduced Paneth cell function, a deficiency that was further aggravated by LPD. Separate supplementation with NAD+ precursors or bile acid sequestrant partially restored LPD-associated Paneth cell defects and, when combined, fully restored all histopathology defects in LPD-fed mice. Therapeutic regimens that increase protein and NAD+ contents while reducing excessive bile acids may benefit children with refractory EED. Dysregulation in bile acid signaling and NAD+ synthesis cause environmental enteric dysfunction. Editor’s summary Environmental enteric dysfunction (EED) is an acquired condition of the small intestine that is associated with inflammation, poor nutrient absorption, and stunted growth in children. Malique et al. examined a cohort of children with EED and found pathophysiological changes in the cellular composition of the small bowel as well as increased serum bile acids and decreased nicotinamide adenine dinucleotide (NAD+). Work in a mouse model of EED suggested that increased dietary protein and NAD+ as well as reduction of bile acids should be further investigated as potential therapeutic avenues for this disorder. —Catherine Charneski
Adjuvanted SARS-CoV-2 spike protein vaccination elicits long-lived plasma cells in nonhuman primates

Durable humoral immunity is mediated by long-lived plasma cells (LLPCs) that reside in the bone marrow. It remains unclear whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein vaccination is able to elicit and maintain LLPCs. Here, we describe a sensitive method to identify and isolate antigen-specific LLPCs by tethering antibodies secreted by these cells onto the cell surface. Using this method, we found that two doses of adjuvanted SARS-CoV-2 spike protein vaccination are able to induce spike protein–specific LLPC reservoirs enriched for receptor binding domain specificities in the bone marrow of nonhuman primates that are detectable for several months after vaccination. Immunoglobulin gene sequencing confirmed that several of these LLPCs were clones of memory B cells elicited 2 weeks after boost that had undergone further somatic hypermutation. Many of the antibodies secreted by these LLPCs also exhibited improved neutralization and cross-reactivity compared with earlier time points. These findings establish our method as a means to sensitively and reliably detect rare antigen-specific LLPCs and demonstrate that adjuvanted SARS-CoV-2 spike protein vaccination establishes spike protein–specific LLPC reservoirs. Adjuvanted SARS-CoV-2 spike protein vaccination establishes spike protein–specific IgG-secreting long-lived plasma cells in the bone marrow. Editor’s summary Although much is known about the antibody response to SARS-CoV-2 vaccination, relatively little is known about the elusive cells that make the antibodies. These long-lived plasma cells (LLPCs) reside in bone marrow and, as a consequence, are challenging to study in humans. To better understand these cells in a related species, Prabhakaran et al. longitudinally characterized bone marrow LLPCs in a cohort of nonhuman primates immunized with an adjuvanted spike protein vaccine. Using an antigen-specific LLPC isolation technique, the authors purified spike protein– and receptor binding domain–specific LLPCs from the bone marrow of the animals and determined that their antibody repertoires matched those of memory B cells in the peripheral blood. They also observed that antibodies generated by these LLPCs had undergone somatic hypermutation, which was associated with improved neutralizing and binding capabilities at later time points. Together, these results report a technique to reliably isolate antigen-specific LLPCs and further demonstrate that LLPCs are generated by a SARS-CoV-2 protein vaccine. —Courtney Malo
The military gear microbiome: risk factors surrounding the warfighter

ABSTRACT Combat extremity wounds are highly susceptible to contamination from surrounding environmental material. This bioburden could be partially transferred from materials in immediate proximity to the wound, including fragments of the uniform and gear. However, the assessment of the microbial bioburden present on military gear during operational conditions of deployment or training is relatively unexplored. Opportunistic pathogens that can survive on gear represent risk factors for infection following injury, especially following combat blasts, where fibers and other materials are embedded in wounded tissue. We utilized 16S rRNA sequencing to assess the microbiome composition of different military gear types (boot, trouser, coat, and canteen) from two operational environments (training in Hawai’i and deployed in Indonesia) across time (days 0 and 14). We found that microbiome diversity, stability, and composition were dependent on gear type, training location, and sampling timepoint. At day 14, species diversity was significantly higher in Hawai’i samples compared to Indonesia samples for boot, coat, and trouser swabs. In addition, we observed the presence of potential microbial risk factors, as opportunistic pathogenic species, such as Acinetobacter, Pseudomonas, and Staphylococcus, were found to be present in all sample types and in both study sites. These study outcomes will be used to guide the design of antimicrobial materials and uniforms and for infection control efforts following combat blasts and other injuries, thereby improving treatment guidance during military training and deployment. IMPORTANCE Combat extremity wounds are vulnerable to contamination from environments of proximity to the warfighter, leading to potential detrimental outcomes such as infection and delayed wound healing. Therefore, microbial surveillance of such environments is necessary to aid the advancement of military safety and preparedness through clinical diagnostics, treatment protocols, and uniform material design. Combat extremity wounds are vulnerable to contamination from environments of proximity to the warfighter, leading to potential detrimental outcomes such as infection and delayed wound healing. Therefore, microbial surveillance of such environments is necessary to aid the advancement of military safety and preparedness through clinical diagnostics, treatment protocols, and uniform material design.
Improved characterization of aminoglycoside penetration into human lung epithelial lining fluid via population pharmacokinetics

ABSTRACT Aminoglycosides are important treatment options for serious lung infections, but modeling analyses to quantify their human lung epithelial lining fluid (ELF) penetration are lacking. We estimated the extent and rate of penetration for five aminoglycosides via population pharmacokinetics from eight published studies. The area under the curve in ELF vs plasma ranged from 50% to 100% and equilibration half-lives from 0.61 to 5.80 h, indicating extensive system hysteresis. Aminoglycoside ELF peak concentrations were blunted, but overall exposures were moderately high.
Leaf-Litter Anurans in An Agricultural Landscape in the araucaria Forest: A Case Study for Southern Brazil

Abstract. The conversion of natural environments into extensive agricultural monocultures is one of the principal drivers of the ongoing decline and extinction of anuran populations. These processes of landscape modification may modify the structure of natural communities in a variety of ways and at a number of different levels. In this case study, we aimed to evaluate the taxonomic and functional composition of leaf-litter anurans in an agricultural landscape in southern Brazil to understand the relationship between the ecological and morphological traits of these vertebrates and the environment. We sampled forest and plantation environments using pitfall traps and described and quantified the ecological and morphological traits of all the individuals collected in these two environments. We used permutational multivariate analyses of variance (perMANOVA) to evaluate the variation in the composition in the anuran assemblage between the forest and the plantation, a principal coordinate analysis (PCoA) to test the relationship between these traits and the environments, and generalized linear mixed models (GLMM) to evaluate possible patterns of differentiation in the morphological traits of the anurans in the different habitats. Our results indicated that no significant variation exists in the taxonomic and functional composition of the forest and plantation environments, and the functional structure varied only in relation to the body length of the specimens. Although this was a small-scale case study, we found that the anurans tended to exploit different environments according to the functional characteristics of their reproductive modes, habits, and life cycles.
Reproductive Choices Lead Tadpole Survival in Lepidobatrachus llanensis and Pleurodema guayapae

Abstract. Selection of breeding habitats represents one of the most effective ways in which anurans can offer advantages for offspring survival. In arid environments tadpoles developing in ephemeral pools face different risks for survival such as pond drying, predation, high levels of salinity, and exposure to high temperatures. Therefore, in these environments adults should actively select ponds with biotic and abiotic attributes that favor tadpole survival. In this work, we evaluate whether two frog species inhabiting halophytic shrublands from Dry Chaco (Pleurodema guayapae and Lepidobatrachus llanensis) select pond attributes for reproduction and whether selection favors the success of tadpoles on reaching metamorphosis. We recorded the use/non-use of ephemeral pools for reproduction in both species and measured five pond attributes to detect selection of breeding sites. Next, we monitored a subset of sites used for reproduction in each species in order to relate metamorphosis success with the same pond attributes. The results showed that L. llanensis selected ponds with particular attributes, while P. guayapae laid its eggs in any available pond, even ponds that dried prior to metamorphosis. Using any pond available in multiple reproductive events together with the remarkably fast development of tadpoles could actually be a strategy to effectively deal with stochasticity in rain frequencies after the formation of ephemeral ponds.
Bioclimatic predictors of forest structure, composition and phenology in the Paraguayan Dry Chaco

Abstract One of the largest remnants of tropical dry forest is the South American Gran Chaco. A quarter of this biome is in Paraguay, but there have been few studies in the Paraguayan Chaco. The Gran Chaco flora is diverse in structure, function, composition and phenology. Fundamental ecological questions remain in this biome, such as what bioclimatic factors shape the Chaco’s composition, structure and phenology. In this study, we integrated forest inventories from permanent plots with monthly high-resolution NDVI from PlanetScope and historical climate data from WorldClim to identify bioclimatic predictors of forest structure, composition and phenology. We found that bioclimatic variables related to precipitation were correlated with stem density and Pielou evenness index, while temperature-related variables correlated with basal area. The best predictor of forest phenology (NDVI variation) was precipitation lagged by 1 month followed by temperature lagged by 2 months. In the period with most water stress, the phenological response correlates with diversity, height and basal area, showing links with dominance and tree size. Our results indicate that even if the ecology and function of Dry Chaco Forest is characterised by water limitation, temperature has a moderating effect by limiting growth and influencing leaf flush and deciduousness.
The crystal structure of ethyl (E)-1-chloro-3-(4-chloro-1-methyl-1H-indole-2-carbonyl)-4-oxo-2-phenylcyclooct-2-ene-1-carboxylate, C27H25Cl2NO4

Abstract C27H25Cl2NO4, monoclinic, P21/n (no. 14), a = 18.9834(7) Å, b = 8.5683(3) Å, c = 30.4308(12) Å, β = 93.832(1)°, V = 4938.7(3) Å3, Z = 8, R gt (F) = 0.0655, wR ref (F2) = 0.2089, T = 296(2) K.
Cost-Sensitive Trees for Interpretable Reinforcement Learning

Trees have emerged as the most popular choice of intrinsically interpretable models to represent policies in reinforcement learning. However, directly learning a tree policy poses challenges, prompting existing approaches to employ neural network policies to generate datasets for training tree-based models in a supervised manner. Nonetheless, these approaches assume that the action suggested by the neural network policy represents the sole optimal action, with all other actions being equally sub-optimal. This work presents a novel perspective by associating different costs with the prediction of different actions. By adopting a cost-sensitive approach to tree construction, we demonstrate that policies generated using this methodology exhibit improved performance. To validate our findings, we develop cost-sensitive variants of two established methods, VIPER and MoET, and provide empirical evidence showcasing their superiority over the original methods across diverse environments.
IndicOCR: A Pipeline for Recognizing Printed Documents for Indian Languages

India’s linguistic diversity is a testament to the cultural richness of the subcontinent, with 22 officially recognized languages, each possessing its unique script and identity. In this context, the need for efficient Optical Character Recognition (OCR) tools tailored to the complexities of these languages is paramount. IndicOCR is introduced as an innovative OCR pipeline designed to address this challenge. This paper aims to shed light on the capabilities of IndicOCR, underlining its role in bridging the gap between India’s linguistic heritage and the ever-expanding digital world. Finally, IndicOCR1 is a powerful tool for inclusivity, preservation, and progress in a linguistically diverse society.
Improved Representations using Augmented Graphs

Node classification has become an essential problem with several real-world applications. In the last few years, graph neural networks have been widely used for classifying nodes on large networks. However, the success of existing methods is found to be limited while classifying low-degree nodes. In this paper, we propose a suite of neighborhood augmentation strategies that enhance the general classification accuracy of graph neural networks as well as improve the results for the aforementioned nodes. On top of the structural neighborhood, here we incorporate the notion of representation space neighborhood for further improving node representations. In particular, with the help of locality-sensitive hashing, we propose a scalable method to augment nodes with their representation space neighbors. Our LSH-Stack formulation resulted in 6.6% relative improvement in Micro F1 scores on average in graph benchmarks over GraphSAGE GNN. Finally, we discuss how our proposed neighborhood augmentation strategy is generic and can be used to improve the quality of the learned representation in other domains such as images, where there is no graph structure to begin with.
Information spread in opinionated social networks

The proposed work develops a framework for opinion-aware network formulation from online topical networks and its subsequent analysis. Novel concepts of opinion class and opinion compatibility are proposed for a context-aware graph-reduce approach. Experiments highlight how the context-sensitive framework enables faster influence maximisation over the network using existing Greedy and CELF models of influence spread.
BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models

We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO.