Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Inference for Scalable 3D Object-centric Learning

We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes. Existing approaches to object-centric representation learning show limitations in generalizing to larger scenes as their learning processes rely on a fixed global coordinate system. In contrast, we propose to learn view-invariant 3D object representations in localized object coordinate systems. To this end, we estimate the object pose and appearance representation separately and explicitly map object representations across views while maintaining object identities. We adopt an amortized variational inference pipeline that can process sequential input and scalably update object latent distributions online. To handle large-scale scenes with a varying number of objects, we further introduce a Cognitive Map that allows the registration and query of objects on a per-scene global map to achieve scalable representation learning. We explore the object-centric neural radiance field (NeRF) as our 3D scene representation, which is jointly modeled within our unsupervised object-centric learning framework. Experimental results on synthetic and real datasets show that our proposed method can infer and maintain object-centric representations of 3D scenes and outperforms previous models.
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bounds on the learning error are essentially vacuous and greatly mismatch empirical observations. This is because the heterogeneity model considered is too restrictive and does not cover basic learning tasks such as least-squares regression. We consider in this paper a more realistic heterogeneity model, namely (G,B)-gradient dissimilarity, and show that it covers a larger class of learning problems than existing theory. Notably, we show that the breakdown point under heterogeneity is lower than the classical fraction 1/2. We also prove a new lower bound on the learning error of any distributed learning algorithm. We derive a matching upper bound for a robust variant of distributed gradient descent, and empirically show that our analysis reduces the gap between theory and practice.
FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes

3D coverage path planning for UAVs is a crucial problem in diverse practical applications. However, existing methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex scenes. To address these challenges, we propose FC-Planner, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing. We decompose the scene into several simple subspaces by a skeleton-based space decomposition (SSD). Additionally, the skeleton guides us to effortlessly determine free space. We utilize the skeleton to efficiently generate a minimal set of specialized and informative viewpoints for complete coverage. Based on SSD, a hierarchical planner effectively divides the large planning problem into independent sub-problems, enabling parallel planning for each subspace. The carefully designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in path generation. We conduct extensive benchmark and real-world tests, where FC-Planner computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage. The source code will be made publicly available to benefit the community. Project page: https://hkust-aerial-robotics.github. io/FC-Planner.
Effect of layer age, room temperature storage period and strain of hen on egg quality and egg microbiology during summer.

This study was aimed to evaluate the influence of hen layer age and storage periods at room temperature (33.2°C) of white and brown hy-line strains during summer conditions. A 800 eggs were used, 400 from each layer age (200 white and 200 brown) either white or brown were divided in to five groups according to storage periods. Eggs from white and brown Hy-line hens at 43 and 63 weeks of age were sampled immediately after lay and after periods of storage of 7,14,21 and 28 days. Eggs were individually weighed. Every storage period within each age had 6 egg from each strain for microbiology study. The yolk of each egg was cautiously separated from albumen and weighed. Albumen weight was calculated by the difference. Shell thickness was measured using a shell thickness gauge at three different locations on the egg shell. Albumen height was measured using a micrometer. The pH of albumen was measured using pH meter. Eggs from the brown strain were 3.73% heavier, had 4.21% more eggshell, and had a higher specific gravity than the white strain eggs (1.079 and 1.073, respectively). Egg weight increased with increasing hen age and the yolk increased more in size than did the shell and albumen. The albumen height of eggs from Hy-line brown was greater than that of Hy-line white hens, and it decreased as the hen age increased and with increasing of storage time. The pH of the albumen was affected by strain and hen age, but it increased with time in storage. Longer periods of storage resulted in significantly (P<0.05) lower albumen weight and albumen height and higher albumen pH across the two age groups. Within each line and storage period, the egg weight was more closely associated with albumen weight than with yolk or shell weight. The loss in weight from shell, albumen and yolk was increased with increasing storage period and age of hens. The changes in egg composition with storage time and age were solely the consequence of weight loss from the albumen. According to microbial contents of egg during the 28 days of storage, it seems that the shelf life for white Hy-line and younger age continued through 28 days. While the maximum shelf life for brown and older did not exceed 21 days of storage.
Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions

Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness.
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies

Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for emerging forms of Web search such as AI-driven chat. To understand user intents from log data, we need a way to label them with meaningful categories that capture their diversity and dynamics. Existing methods rely on manual or machine-learned labeling, which are either expensive or inflexible for large and dynamic datasets. We propose a novel solution using large language models (LLMs), which can generate rich and relevant concepts, descriptions, and examples for user intents. However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop. To address this, we propose a new methodology with human experts and assessors to verify the quality of the LLM-generated taxonomy. We also present an end-to-end pipeline that uses an LLM with human-in-the-loop to produce, refine, and apply labels for user intent analysis in log data. We demonstrate its effectiveness by uncovering new insights into user intents from search and chat logs from the Microsoft Bing commercial search engine. The proposed work's novelty stems from the method for generating purpose-driven user intent taxonomies with strong validation. This method not only helps remove methodological and practical bottlenecks from intent-focused research, but also provides a new framework for generating, validating, and applying other kinds of taxonomies in a scalable and adaptable way with minimal human effort.
Fantastic Generalization Measures are Nowhere to be Found

We study the notion of a generalization bound being uniformly tight, meaning that the difference between the bound and the population loss is small for all learning algorithms and all population distributions. Numerous generalization bounds have been proposed in the literature as potential explanations for the ability of neural networks to generalize in the overparameterized setting. However, in their paper ``Fantastic Generalization Measures and Where to Find Them,'' Jiang et al. (2020) examine more than a dozen generalization bounds, and show empirically that none of them are uniformly tight. This raises the question of whether uniformly-tight generalization bounds are at all possible in the overparameterized setting. We consider two types of generalization bounds: (1) bounds that may depend on the training set and the learned hypothesis (e.g., margin bounds). We prove mathematically that no such bound can be uniformly tight in the overparameterized setting; (2) bounds that may in addition also depend on the learning algorithm (e.g., stability bounds). For these bounds, we show a trade-off between the algorithm's performance and the bound's tightness. Namely, if the algorithm achieves good accuracy on certain distributions, then no generalization bound can be uniformly tight for it in the overparameterized setting. We explain how these formal results can, in our view, inform research on generalization bounds for neural networks, while stressing that other interpretations of these results are also possible.
Laser scanning optical investigations of dengue virus infection from antibody fluorescence of human blood

Dengue is the most important arthropod-borne viral disease of public health significance today, and the geographic distribution includes more than 100 countries worldwide. A definite diagnosis of dengue virus infection is based on clinical criteria supported by antibody tests of whole blood. In this study, we report on dengue infected whole blood diagnostics using laser scanning confocal microscopy (LSCM) over a laser excitation of 488, 543 and 633 nm wavelengths. Based on our findings, the system has potential applications in the detection and quantification of dengue virus infected cells, antigens and antibodies in blood in vitro. Analysis of about 100 infected patients shows that the primary organism (or pathogen), characteristic of dengue infection, causes a significant biochemical change in the nature of human blood in which it resides, and, due to which, fluorescence increases in dengue samples. Thus, the use of a reliable and sensitive rapid diagnostic test using LSCM can simultaneously detect the presence of anti-dengue IgG and IgM antibodies, which is a breakthrough in diagnosis and of great clinical use. The antibodies for dengue continue to be distinguishable for a couple of months after infection. This change in blood fluorescence may be a significant contribution from light scattering through antibodies produced against the dengue antigen.
A unified worst case for classical simplex and policy iteration pivot rules

We construct a family of Markov decision processes for which the policy iteration algorithm needs an exponential number of improving switches with Dantzig's rule, with Bland's rule, and with the Largest Increase pivot rule. This immediately translates to a family of linear programs for which the simplex algorithm needs an exponential number of pivot steps with the same three pivot rules. Our results yield a unified construction that simultaneously reproduces well-known lower bounds for these classical pivot rules, and we are able to infer that any (deterministic or randomized) combination of them cannot avoid an exponential worst-case behavior. Regarding the policy iteration algorithm, pivot rules typically switch multiple edges simultaneously and our lower bound for Dantzig's rule and the Largest Increase rule, which perform only single switches, seem novel. Regarding the simplex algorithm, the individual lower bounds were previously obtained separately via deformed hypercube constructions. In contrast to previous bounds for the simplex algorithm via Markov decision processes, our rigorous analysis is reasonably concise.
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks

In this paper, we propose (i) a novel distributed algorithm for consensus optimization over networks and (ii) a robust extension tailored to deal with asynchronous agents and packet losses. The key idea is to achieve dynamic consensus on (i) the agents' average and (ii) the global descent direction by iteratively solving an online auxiliary optimization problem through a distributed implementation of the Alternating Direction Method of Multipliers (ADMM). Such a mechanism is suitably interlaced with a local proportional action steering each agent estimate to the solution of the original consensus optimization problem. First, in the case of ideal networks, by using tools from system theory, we prove the linear convergence of the scheme with strongly convex costs. Then, by exploiting the averaging theory, we extend such a first result to prove that the robust extension of our method preserves linear convergence in the case of asynchronous agents and packet losses. Further, by using the notion of Input-to-State Stability, we also guarantee the robustness of the schemes with respect to additional, generic errors affecting the agents' updates. Finally, some numerical simulations confirm our theoretical findings and show that the proposed methods outperform the existing state-of-the-art distributed methods for consensus optimization.
Implications of low volatility SOA and gas‐phase fragmentation reactions on SOA loadings and their spatial and temporal evolution in the atmosphere

We investigate issues related to volatility and multi‐generational gas‐phase aging parameterizations affecting the formation and evolution of secondary organic aerosol (SOA) in models. We show that when assuming realistic values for the mass accommodation coefficient, experimentally observed SOA evaporation rates imply significantly lower “effective volatility” than those derived from SOA growth in smog chambers, pointing to the role of condensed phase processes and suggesting that models need to use different parameters to describe the formation and evolution of SOA. We develop a new, experimentally driven paradigm to represent SOA as a non‐absorbing semi‐solid with very low “effective volatility. ” We modify both a box model and a 3D chemical transport model, to include simplified parameterizations capturing the first‐order effects of gas‐phase fragmentation reactions and investigate the implications of treating SOA as a non‐volatile, non‐absorbing semi‐solid (NVSOA). Box model simulations predict SOA loadings decrease with increasing fragmentation, and similar SOA loadings are calculated in the traditional, semi‐volatile (SVSOA) approach and with the new paradigm (NVSOA) before evaporation reduces loadings of SVSOA. Box‐model‐calculated O:C ratios increase with aging in both the SVSOA and the NVSOA paradigms. Consistent with box model results, 3D model simulations demonstrate that predicted SOA loadings decrease with the addition of fragmentation reactions. The NVSOA paradigm predicts higher SOA loadings compared to the SVSOA paradigm over nearly the entire 3D modeling domain, with larger differences close to the surface and in regions where higher dilution favors SVSOA evaporation.
UniHead: Unifying Multi-Perception for Detection Heads

The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used parallel head often lacks omni perceptual capabilities, such as deformation perception, global perception and cross-task perception. Despite numerous methods attempt to enhance these abilities from a single aspect, achieving a comprehensive and unified solution remains a significant challenge. In response to this challenge, we have developed an innovative detection head, termed UniHead, to unify three perceptual abilities simultaneously. More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks. As a plug-and-play method, the proposed UniHead can be conveniently integrated with existing detectors. Extensive experiments on the COCO dataset demonstrate that our UniHead can bring significant improvements to many detectors. For instance, the UniHead can obtain +2.7 AP gains in RetinaNet, +2.9 AP gains in FreeAnchor, and +2.1 AP gains in GFL. The code will be publicly available. Code Url: https://github.com/zht8506/UniHead.
The Influence of Soil Organic Matter Age Spectrum on the Reconstruction of Atmospheric 14C Levels Via Stalagmites

The imprint of the radiocarbon bomb peak was detected in the top of stalagmite ER-77 from Grotta di Ernesto (NE Italy). This recently grown stalagmite reveals a reservoir age, also known as dead carbon fraction (dcf), of ≃1050 14C yr, or 12%. By applying a 14C soil-karst model, the age spectrum of soil organic matter (SOM) as well as the CO2 contribution of the single SOM reservoirs to the total soil CO2 can be derived. Under the assumption of constant vegetation, meaning both vegetation density and the age spectrum of SOM, it is possible to derive the soil-air 14C activity of the past using the 14C calibration curve (IntCal04). Hence, it is also possible to calculate an artificial stalagmite 14C data set covering the last 25,000 yr with parameters determined for stalagmite ER-77. With this artificially constructed data set, we derived the hypothetical atmospheric 14C activity by using the common method of applying a constant dcf on the modeled 14C data set of the stalagmite. This theoretical approach allows to analyze the impact of a constant and variable SOM age spectrum on atmospheric 14C reconstructions performed with real stalagmite 14C measurements. We observe deviations between IntCal04 and the atmospheric 14C activity as derived with our modeled 14C data set, which are larger for older SOM than for younger SOM and vary in time up to 2 pMC, depending on the strength of the variations in the atmospheric 14C level. This value is comparable with the 1-σ uncertainty given by IntCal04 for the last glacial. For a varying SOM age spectrum, the deviations between the calibration curve and 14C level of the atmosphere reconstructed with a stalagmite exceed 3 pMC, which is larger than the 1-σ uncertainty of IntCal04. In general, the SOM has smoothing, shifting, and 14C-depleting effects on the stalagmite 14C record and, therefore, on the stalagmite-derived atmospheric 14C activity. In this study, changes in soil-air pCO2 and carbonate dissolution conditions, which have also an important impact on the 14C record of a stalagmite, are not accounted for.
High order approximation to Caputo derivative on graded mesh and time-fractional diffusion equation for non-smooth solutions

In this paper, a high-order approximation to Caputo-type time-fractional diffusion equations involving an initial-time singularity of the solution is proposed. At first, we employ a numerical algorithm based on the Lagrange polynomial interpolation to approximate the Caputo derivative on the non-uniform mesh. Then truncation error rate and the optimal grading constant of the approximation on a graded mesh are obtained as $\min\{4-\alpha,r\alpha\}$ and $\frac{4-\alpha}{\alpha}$, respectively, where $\alpha\in(0,1)$ is the order of fractional derivative and $r\geq 1$ is the mesh grading parameter. Using this new approximation, a difference scheme for the Caputo-type time-fractional diffusion equation on graded temporal mesh is formulated. The scheme proves to be uniquely solvable for general $r$. Then we derive the unconditional stability of the scheme on uniform mesh. The convergence of the scheme, in particular for $r=1$, is analyzed for non-smooth solutions and concluded for smooth solutions. Finally, the accuracy of the scheme is verified by analyzing the error through a few numerical examples.
PRIS: Practical robust invertible network for image steganography

Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes and can be recovered when needed. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. Such as Gaussian noise and lossy compression. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks, and put two enhance modules before and after the extraction process with a 3-step training strategy. Moreover, rounding error is considered which is always ignored by existing methods, but actually it is unavoidable in practical. A gradient approximation function (GAF) is also proposed to overcome the undifferentiable issue of rounding distortion. Experimental results show that our PRIS outperforms the state-of-the-art robust image steganography method in both robustness and practicability. Codes are available at https://github.com/yanghangAI/PRIS, demonstration of our model in practical at http://yanghang.site/hide/.