Miracle-dz/UniFAR-FTUs huggingface dataset:
SLS4D: Sparse Latent Space for 4D Novel View Synthesis

Neural radiance field (NeRF) has achieved great success in novel view synthesis and 3D representation for static scenarios. Existing dynamic NeRFs usually exploit a locally dense grid to fit the deformation field; however, they fail to capture the global dynamics and concomitantly yield models of heavy parameters. We observe that the 4D space is inherently sparse. Firstly, the deformation field is sparse in spatial but dense in temporal due to the continuity of of motion. Secondly, the radiance field is only valid on the surface of the underlying scene, usually occupying a small fraction of the whole space. We thus propose to represent the 4D scene using a learnable sparse latent space, a.k. a. SLS4D. Specifically, SLS4D first uses dense learnable time slot features to depict the temporal space, from which the deformation field is fitted with linear multi-layer perceptions (MLP) to predict the displacement of a 3D position at any time. It then learns the spatial features of a 3D position using another sparse latent space. This is achieved by learning the adaptive weights of each latent code with the attention mechanism. Extensive experiments demonstrate the effectiveness of our SLS4D: it achieves the best 4D novel view synthesis using only about $6\%$ parameters of the most recent work.
Guided moments formalism: A new efficient full-neutrino treatment for astrophysical simulations

We present the new Guided Moments ($\texttt{GM}$) formalism for neutrino modeling in astrophysical scenarios like core-collapse supernovae and neutron star mergers. The truncated moments approximation ($\texttt{M1}$) and Monte-Carlo ($\texttt{MC}$) schemes have been proven to be robust and accurate in solving the Boltzmann's equation for neutrino transport. However, it is well-known that each method exhibits specific strengths and weaknesses in various physical scenarios. The $\texttt{GM}$ formalism effectively solves these problems, providing a comprehensive scheme capable of accurately capturing the optically thick limit through the exact $\texttt{M1}$ closure and the optically thin limit through a $\texttt{MC}$ based approach. In addition, the $\texttt{GM}$ method also approximates the neutrino distribution function with a reasonable computational cost, which is crucial for the correct estimation of the different neutrino-fluid interactions. Our work provides a comprehensive discussion of the formulation and application of the $\texttt{GM}$ method, concluding with a thorough comparison across several test problems involving the three schemes ($\texttt{M1}$, $\texttt{MC}$, $\texttt{GM}$) under consideration.
Tight Hamilton cycles with high discrepancy

In this paper, we initiate the study of discrepancy questions for spanning subgraphs of $k$-uniform hypergraphs. Our main result is that any $2$-colouring of the edges of a $k$-uniform $n$-vertex hypergraph $G$ with minimum $(k-1)$-degree $\delta(G) \ge (1/2+o(1))n$ contains a tight Hamilton cycle with high discrepancy, that is, with at least $n/2+\Omega(n)$ edges of one colour. The minimum degree condition is asymptotically best possible and our theorem also implies a corresponding result for perfect matchings. Our tools combine various structural techniques such as Tur\'an-type problems and hypergraph shadows with probabilistic techniques such as random walks and the nibble method. We also propose several intriguing problems for future research.
Arm exponent for the Gaussian free field on metric graphs in intermediate dimensions

We investigate the bond percolation model on transient weighted graphs ${G}$ induced by the excursion sets of the Gaussian free field on the corresponding metric graph. We assume that balls in ${G}$ have polynomial volume growth with growth exponent $\alpha$ and that the Green's function for the random walk on ${G}$ exhibits a power law decay with exponent $\nu$, in the regime $1\leq \nu \leq \frac{\alpha}{2}$. In particular, this includes the cases of ${G}=\mathbb Z^3$, for which $\nu=1$, and ${G}= \mathbb Z^4$, for which $\nu=\frac{\alpha}{2}=2$. For all such graphs, we determine the leading-order asymptotic behavior for the critical one-arm probability, which we prove decays with distance $R$ like $R^{-\frac{\nu}{2}+o(1)}$. Our results are in fact more precise and yield logarithmic corrections when $\nu>1$ as well as corrections of order $\log \log R$ when $\nu=1$. We further obtain very sharp upper bounds on truncated two-point functions close to criticality, which are new when $\nu>1$ and essentially optimal when $\nu=1$. This extends previous results from arXiv:2101.05801.
The Effects of Climate Change and Variability on Food Security in Zimbabwe: A Socio-Economic and Political Analysis

The paper examines the widespread effects of climate change and variability on food security in Zimbabwe. Climate change has posed a serious threat on food security in developing countries in Africa. This has led developing countries to heavily rely on foreign aid in the form of food hand-outs to avert hunger. The shift in climatic conditions over the sub-Saharan region towards semi-arid to arid conditions has stemmed up a lot of concern as to whether Africa can feed itself. The past three decades have been characterized by an erratic rainfall pattern over Africa’s sub-tropics and a significant decline in the amount of rainfall. This has resulted in droughts which have significantly affected agriculture and food production. Crops and livestock have failed to quickly adapt to these harsh climatic conditions. Research on the impacts of climate change in Zimbabwe shows that the country’s agricultural sector is already suffering from changing rainfall patterns, temperature increases and more extreme weather events, like floods and droughts. Longer and more frequent droughts have substantially reduced crop yields and this has negatively impacted food production in the country. A shift in the country’s agroecological regions has been observed and this shift has been attributed to climate change. The paper analyses the various drought mitigation measures that the government of Zimbabwe has put in place to avert the effects of climate change. It has become apparently clear that the only way Zimbabwe will be able to out-run the effects of climate change on food security would be to scale up production in the agricultural sector by setting up schemes to assist farmers so that they attain the maximum crop yields. These measures are analyzed, looking at their strengths and shortfalls. The paper takes an interesting turn as it analyses the social impact of climate change and its ability to influence changes in Zimbabwe’s political landscape. The shortage of food and depletion of water resources has a greater chance of triggering the escalation of conflict which may influence shifts in the country’s balance of power.
Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception

Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-train models on large-scale classification datasets and then fine-tune them for specific downstream tasks. However, they neglect the specific characteristics of vehicle perception in different tasks and might thus lead to sub-optimal performance. To address this issue, we propose a novel vehicle-centric pre-training framework called VehicleMAE, which incorporates the structural information including the spatial structure from vehicle profile information and the semantic structure from informative high-level natural language descriptions for effective masked vehicle appearance reconstruction. To be specific, we explicitly extract the sketch lines of vehicles as a form of the spatial structure to guide vehicle reconstruction. The more comprehensive knowledge distilled from the CLIP big model based on the similarity between the paired/unpaired vehicle image-text sample is further taken into consideration to help achieve a better understanding of vehicles. A large-scale dataset is built to pre-train our model, termed Autobot1M, which contains about 1M vehicle images and 12693 text information. Extensive experiments on four vehicle-based downstream tasks fully validated the effectiveness of our VehicleMAE. The source code and pre-trained models will be released at https://github.com/Event-AHU/VehicleMAE.
High angular sensitivity X-ray phase-contrast microtomography of soft-tissue through a two-directional beam-tracking synchrotron set-up

Two-directional beam-tracking (2DBT) is a method for phase-contrast imaging and tomography that uses an intensity modulator to structure the X-ray beam into an array of independent circular beamlets that are resolved by a high-resolution detector. It features isotropic spatial resolution, provides two-dimensional phase sensitivity, and enables the three-dimensional reconstructions of the refractive index decrement, $\delta$, and the attenuation coefficient, $\mu$. In this work, we report on the angular sensitivity and the spatial resolution of 2DBT images in a synchrotron-based implementation. In its best configuration, we obtained angular sensitivities of $\sim$20 $n$rad and spatial resolution of at least 6.25 $\mu$m in phase-contrast images. We also demonstrate exemplar application to the three-dimensional imaging of soft tissue samples, including a mouse liver and a decellularised porcine dermis.
Uncertainty Quantification in Machine Learning for Biosignal Applications - A Review

Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electroocculography (EOG) and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal to noise ratio, and good human interpretability is pivotal for medical applications and Brain Computer Interfaces. In this paper, we review the state of the art at the intersection of Uncertainty Quantification and Biosignal with Machine Learning. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty model in a (clinical) environment.
Sparsity meets correlation in Gaussian sequence model

We study estimation of an $s$-sparse signal in the $p$-dimensional Gaussian sequence model with equicorrelated observations and derive the minimax rate. A new phenomenon emerges from correlation, namely the rate scales with respect to $p-2s$ and exhibits a phase transition at $p-2s \asymp \sqrt{p}$. Correlation is shown to be a blessing provided it is sufficiently strong, and the critical correlation level exhibits a delicate dependence on the sparsity level. Due to correlation, the minimax rate is driven by two subproblems: estimation of a linear functional (the average of the signal) and estimation of the signal's $(p-1)$-dimensional projection onto the orthogonal subspace. The high-dimensional projection is estimated via sparse regression and the linear functional is cast as a robust location estimation problem. Existing robust estimators turn out to be suboptimal, and we show a kernel mode estimator with a widening bandwidth exploits the Gaussian character of the data to achieve the optimal estimation rate.
Variational excess risk bound for general state space models

In this paper, we consider variational autoencoders (VAE) for general state space models. We consider a backward factorization of the variational distributions to analyze the excess risk associated with VAE. Such backward factorizations were recently proposed to perform online variational learning and to obtain upper bounds on the variational estimation error. When independent trajectories of sequences are observed and under strong mixing assumptions on the state space model and on the variational distribution, we provide an oracle inequality explicit in the number of samples and in the length of the observation sequences. We then derive consequences of this theoretical result. In particular, when the data distribution is given by a state space model, we provide an upper bound for the Kullback-Leibler divergence between the data distribution and its estimator and between the variational posterior and the estimated state space posterior distributions.Under classical assumptions, we prove that our results can be applied to Gaussian backward kernels built with dense and recurrent neural networks.
Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs

Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to problems and fostering collaboration in open-source communities. Effectively identifying such information in the high volume of communications across the different project channels, such as chats, emails, and issue comments, requires automated recognition of emotions and their causes. To enable this automation, large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. However, such datasets are expensive to create with the variety and informal nature of software projects' communication channels. In this paper, we explore zero-shot LLMs that are pre-trained on massive datasets but without being fine-tuned specifically for the task of detecting emotion causes in software engineering: ChatGPT, GPT-4, and flan-alpaca. Our evaluation indicates that these recently available models can identify emotion categories when given detailed emotions, although they perform worse than the top-rated models. For emotion cause identification, our results indicate that zero-shot LLMs are effective at recognizing the correct emotion cause with a BLEU-2 score of 0.598. To highlight the potential use of these techniques, we conduct a case study of the causes of Frustration in the last year of development of a popular open-source project, revealing several interesting insights.
Physical models of bacterial chromosomes

The interplay between bacterial chromosome organization and functions such as transcription and replication can be studied in increasing detail using novel experimental techniques. Interpreting the resulting quantitative data, however, can be theoretically challenging. In this minireview, we discuss how connecting experimental observations to biophysical theory and modeling can give rise to new insights on bacterial chromosome organization. We consider three flavors of models of increasing complexity: simple polymer models that explore how physical constraints, such as confinement or plectoneme branching, can affect bacterial chromosome organization; bottom-up mechanistic models that connect these constraints to their underlying causes, for instance chromosome compaction to macromolecular crowding, or supercoiling to transcription; and finally, data-driven methods for inferring interpretable and quantitative models directly from complex experimental data. Using recent examples, we discuss how biophysical models can both deepen our understanding of how bacterial chromosomes are structured, and give rise to novel predictions about bacterial chromosome organization.
Corona-heated Accretion-disk Reprocessing: Frequency-resolved Lag Predictions for UV/Optical Reverberation Mapping of Active Galactic Nuclei

Continuum reverberation mapping with high-cadence, long-term UV/optical monitoring of active galactic nuclei (AGNs) enables us to resolve the AGN central engine sizes on different timescales. The frequency-resolved time lags of NGC 5548 (the target for the AGN STORM I campaign) are inconsistent with the X-ray reprocessing of the classical Shakura–Sunyaev disk model. Here we show that the frequency-resolved time lags in NGC 5548 can be well produced by the Corona-Heated Accretion-disk Reprocessing (CHAR) model. Moreover, we make CHAR model predictions of the frequency-resolved time lags for Mrk 817, the source of the AGN STORM II campaign. We also obtain frequency-resolved time lags as a function of the BH mass and Eddington ratio, which is valid for BH masses from 106.5 to 109 M ⊙ and Eddington ratios from 0.01 to 1. Moreover, we demonstrate that with the time spans of current continuum reverberation-mapping campaigns, the lag–luminosity relation of the CHAR model can be τ gz ∝ L 5100 0.55 ± 0.04 , which is consistent with observations. Future observations can test our results and shed new light on resolving the AGN central engine.
Quilt: Robust Data Segment Selection against Concept Drifts

Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept drift adaptation approaches mostly focus on updating the model to the new data possibly using ensemble techniques of previous models and tend to discard the drifted historical data. However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy. To address the potential downside of efficiency, Quilt extends existing data subset selection techniques, which can be used to reduce the training data without compromising model accuracy. These techniques cannot be used as is because they only assume virtual drifts where the posterior probabilities P(y|X) are assumed not to change. In contrast, a key challenge in our setup is to also discard undesirable data segments with concept drifts. Quilt thus discards drifted data segments and selects data segment subsets holistically for accurate and efficient model training. The two operations use gradient-based scores, which have little computation overhead. In our experiments, we show that Quilt outperforms state-of-the-art drift adaptation and data selection baselines on synthetic and real datasets.
Collaborating Foundation models for Domain Generalized Semantic Segmentation

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates FMs of various kinds: (i) CLIP backbone for its robust feature representation, (ii) generative models to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged miou, respectively. The code is available at : https://github.com/yasserben/CLOUDS
Efficient Machine Learning Force Field for Large-Scale Molecular Simulations of Organic Systems

To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms. These achievements guarantee high predictive accuracy, fast simulation speed, minimal memory consumption, and robust simulation stability, satisfying the requirements for high-precision and long-time molecular simulations in large-scale organic systems.
Propagation and lensing of gravitational waves in Palatini $f(\hat R)$ gravity

Accelerated expansion of the Universe prompted searches of modified gravity theory beyond general relativity, instead of adding a mysterious dark energy component with exotic physical properties. One such alternative gravity approach is metric-affine Palatini $f(\hat{R})$ theory. By now routine gravitational wave detections have opened a promising avenue of searching for modified gravity effects. Future expected cases of strong lensing of gravitational waves will enhance this opportunity further. In this paper, we present a systematic study of the propagation and gravitational lensing of gravitational waves in Palatini $f(\hat R)$ gravity and compare it with general relativity. Using the WKB approximation we explore the geometric-optical limit of lensing and derive the corrections to the measured luminosity distance of the gravitational source. In addition, we study the lensing by the Singular Isothermal Sphere lens model and show that Palatini $f(\hat{R})$ modifies the lensing potential and hence the deflection angle. Then we show that the lens model and chosen theory of gravity influences the rotation of the gravitational wave polarization plane through the deflection angle. To be more specific we discuss the $f(\hat R)=\hat R+\alpha \hat R^2$ gravity theory and find that the modifications comparing to general relativity are negligible if the upper bound of $\alpha \sim 10^{9} \, $m$^2$ suggested in the literature is adopted. However, this bound is not firmly established and can be updated in the future. Therefore, the results we obtained could be valuable for further metric-affine gravity vs. general relativity tests involving lensing of gravitational waves and comparison of luminosity distances measured from electromagnetic and gravitational wave sources.
Methodologies for Future Vehicular Digital Twins

The role of wireless communications in various domains of intelligent transportation systems is significant; it is evident that dependable message exchange between nodes (cars, bikes, pedestrians, infrastructure, etc.) has to be guaranteed to fulfill the stringent requirements for future transportation systems. A precise site-specific digital twin is seen as a key enabler for the cost-effective development and validation of future vehicular communication systems. Furthermore, achieving a realistic digital twin for dependable wireless communications requires accurate measurement, modeling, and emulation of wireless communication channels. However, contemporary approaches in these domains are not efficient enough to satisfy the foreseen needs. In this position paper, we overview the current solutions, indicate their limitations, and discuss the most prospective paths for future investigation.