Miracle-dz/UniFAR-FTUs huggingface dataset:
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models

Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input. Motivated by this, in this paper, we present the first approach to use diffusion models as a prior for highly accurate 3D facial BRDF reconstruction from a single image. We start by leveraging a high-quality UV dataset of facial reflectance (diffuse and specular albedo and normals), which we render under varying illumination settings to simulate natural RGB textures and, then, train an unconditional diffusion model on concatenated pairs of rendered textures and reflectance components. At test time, we fit a 3D morphable model to the given image and unwrap the face in a partial UV texture. By sampling from the diffusion model, while retaining the observed texture part intact, the model inpaints not only the self-occluded areas but also the unknown reflectance components, in a single sequence of denoising steps. In contrast to existing methods, we directly acquire the observed texture from the input image, thus, resulting in more faithful and consistent reflectance estimation. Through a series of qualitative and quantitative comparisons, we demonstrate superior performance in both texture completion as well as reflectance reconstruction tasks.
Message Passing Neural Networks for Traffic Forecasting

A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Traffic forecasting is interesting because it is complex as the future speed of a road is dependent on a number of different factors. Therefore, to properly forecast traffic, we need a model that is capable of capturing all these different factors. A factor that is missing from the existing works is the node interactions factor. Existing works fail to capture the inter-node interactions because none are using the message-passing flavor of GNN, which is the one best suited to capture the node interactions This paper presents a plausible scenario in road traffic where node interactions are important and argued that the most appropriate GNN flavor to capture node interactions is message-passing. Results from real-world data show the superiority of the message-passing flavor for traffic forecasting. An additional experiment using synthetic data shows that the message-passing flavor can capture inter-node interaction better than other flavors.
StarCoder: may the source be with you!

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA

In this paper we propose a new iterative algorithm to solve the fair PCA (FPCA) problem. We start with the max-min fair PCA formulation originally proposed in [1] and derive a simple and efficient iterative algorithm which is based on the minorization-maximization (MM) approach. The proposed algorithm relies on the relaxation of a semi-orthogonality constraint which is proved to be tight at every iteration of the algorithm. The vanilla version of the proposed algorithm requires solving a semi-definite program (SDP) at every iteration, which can be further simplified to a quadratic program by formulating the dual of the surrogate maximization problem. We also propose two important reformulations of the fair PCA problem: a) fair robust PCA -- which can handle outliers in the data, and b) fair sparse PCA -- which can enforce sparsity on the estimated fair principal components. The proposed algorithms are computationally efficient and monotonically increase their respective design objectives at every iteration. An added feature of the proposed algorithms is that they do not require the selection of any hyperparameter (except for the fair sparse PCA case where a penalty parameter that controls the sparsity has to be chosen by the user). We numerically compare the performance of the proposed methods with two of the state-of-the-art approaches on synthetic data sets and a real-life data set.
FusionBooster: A Unified Image Fusion Boosting Paradigm

In recent years, numerous ideas have emerged for designing a mutually reinforcing mechanism or extra stages for the image fusion task, ignoring the inevitable gaps between different vision tasks and the computational burden. We argue that there is a scope to improve the fusion performance with the help of the FusionBooster, a model specifically designed for the fusion task. In particular, our booster is based on the divide-and-conquer strategy controlled by an information probe. The booster is composed of three building blocks: the probe units, the booster layer, and the assembling module. Given the result produced by a backbone method, the probe units assess the fused image and divide the results according to their information content. This is instrumental in identifying missing information, as a step to its recovery. The recovery of the degraded components along with the fusion guidance are the role of the booster layer. Lastly, the assembling module is responsible for piecing these advanced components together to deliver the output. We use concise reconstruction loss functions in conjunction with lightweight autoencoder models to formulate the learning task, with marginal computational complexity increase. The experimental results obtained in various fusion tasks, as well as downstream detection tasks, consistently demonstrate that the proposed FusionBooster significantly improves the performance. Our code will be publicly available at https://github.com/AWCXV/FusionBooster.
VideoChat: Chat-Centric Video Understanding

In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we build a video-centric instruction dataset, composed of thousands of videos associated with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and captures causal relationships, providing a valuable asset for training our chat-centric video understanding system. Preliminary qualitative experiments demonstrate the potential of our system across a broad spectrum of video applications, which could serve as a simple prototype system for future research on chat-centric video understanding. Access our code and data at https://github.com/OpenGVLab/Ask-Anything
Microscopic theory for hyperuniformity in two-dimensional chiral active fluid

Some nonequilibrium systems exhibit anomalous suppression of the large-scale density fluctuations, so-called hyperuniformity. Recently, hyperuniformity was found numerically in a simple model of chiral active fluids (Lei et al 2019 Sci. Adv. 5 eaau7423). We revisit this phenomenon and put forward a microscopic theory to explain it. An effective fluctuating hydrodynamic equation is derived for a simple particle model of chiral active matter. We show that the linear analysis of the obtained hydrodynamic equation captures hyperuniformity. Our theory yields hyperuniformity characterized by the same exponents as the numerical observation, but the agreement with the numerical data is qualitative. We also argue that the hydrodynamic equation for the effective particle representation, in which each rotating trajectory is regarded as an effective particle, has the same form as the macroscopic description of the random organization model with the center of mass conservation.
Unsupervised Domain Adaptation for Semantic Segmentation via Feature-space Density Matching

Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classiﬁed into one or more predeﬁned semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these semantic classes. Nonetheless, they often fail to generalize when there is a signiﬁcant domain (i.e., distributional) shift between the training (i.e., source ) data and the dataset(s) encountered when deployed (i.e., target ), necessitating manual annotations for the target data to achieve acceptable performance. This is especially important in medical imaging because diﬀerent image modalities have signiﬁcant intra- and inter-site variations due to protocol and vendor variability. Current techniques are sensitive to hyperparameter tun-ing and target dataset size. This paper presents an unsupervised domain adaptation approach for semantic segmentation that alleviates the need for annotating target data. Using kernel density estimation, we match the target data distribution to the source data in the feature space. We demonstrate that our results are comparable or superior on multiple-site prostate MRI and histopathology images, which mitigates the need for annotating target data.
Optimal Eventual Byzantine Agreement Protocols with Omission Failures

Work on optimal protocols for Eventual Byzantine Agreement (EBA)---protocols that, in a precise sense, decide as soon as possible in every run and guarantee that all nonfaulty agents decide on the same value---has focused on full-information protocols (FIPs), where agents repeatedly send messages that completely describe their past observations to every other agent. While it can be shown that, without loss of generality, we can take an optimal protocol to be an FIP, full information exchange is impractical to implement for many applications due to the required message size. We separate protocols into two parts, the information-exchange protocol and the action protocol, so as to be able to examine the effects of more limited information exchange. We then define a notion of optimality with respect to an information-exchange protocol. Roughly speaking, an action protocol P is optimal with respect to an information-exchange protocol ε if, with P, agents decide as soon as possible among action protocols that exchange information according to ε. We present a knowledge-based EBA program for omission failures all of whose implementations are guaranteed to be correct and are optimal if the information exchange satisfies a certain safety condition. We then construct concrete programs that implement this knowledge-based program in two settings of interest that are shown to satisfy the safety condition. Finally, we show that a small modification of our program results in an FIP that is both optimal and efficiently implementable, settling an open problem posed by Halpern, Moses, and Waarts (SIAM J. Comput., 2001).
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs

Vision and language models (VLMs) have demonstrated remarkable zero-shot (ZS) performance in a variety of tasks. However, recent works have shown that even the best VLMs struggle to capture aspects of compositional scene understanding, such as object attributes, relations, and action states. In contrast, obtaining structured annotations, such as scene graphs (SGs), that could improve these models is time-consuming and costly, and thus cannot be used on a large scale. Here we ask whether small SG datasets can provide sufficient information for enhancing structured understanding of pretrained VLMs. We show that it is indeed possible to improve VLMs when learning from SGs by integrating components that incorporate structured information into both visual and textual representations. For the visual side, we incorporate a special"SG Component"in the image transformer trained to predict SG information, while for the textual side, we utilize SGs to generate fine-grained captions that highlight different compositional aspects of the scene. Our method improves the performance of several popular VLMs on multiple VL datasets with only a mild degradation in ZS capabilities.
A Survey of Deep Code Search

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great success in this field. Recently, various deep learning methods, such as graph neural networks and pretraining models, have been applied to code search with significant progress. Deep learning is now the leading paradigm for code search. In this survey, we provide a comprehensive overview of deep learning-based code search. We review the existing deep learning-based code search framework which maps query/code to vectors and measures their similarity. Furthermore, we propose a new taxonomy to illustrate the state-of-the-art deep learning-based code search in a three-steps process: query semantics modeling, code semantics modeling, and matching modeling which involves the deep learning model training. Finally, we suggest potential avenues for future research in this promising field.
Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis

This paper aims for a new generation task: non-stationary multi-texture synthesis, which unifies synthesizing multiple non-stationary textures in a single model. Most non-stationary textures have large scale variance and can hardly be synthesized through one model. To combat this, we propose a multi-scale generator to capture structural patterns of various scales and effectively synthesize textures with a minor cost. However, it is still hard to handle textures of different categories with different texture patterns. Therefore, we present a category-specific training strategy to focus on learning texture pattern of a specific domain. Interestingly, once trained, our model is able to produce multi-pattern generations with dynamic variations without the need to finetune the model for different styles. Moreover, an objective evaluation metric is designed for evaluating the quality of texture expansion and global structure consistency. To our knowledge, ours is the first scheme for this challenging task, including model, training, and evaluation. Experimental results demonstrate the proposed method achieves superior performance and time efficiency. The code will be available after the publication.
An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference

This paper provides a selective review of the statistical network analysis literature focused on clustering and inference problems for stochastic blockmodels and their variants. We survey asymptotic normality results for stochastic blockmodels as a means of thematically linking classical statistical concepts to contemporary research in network data analysis. Of note, multiple different forms of asymptotically Gaussian behavior arise in stochastic blockmodels and are useful for different purposes, pertaining to estimation and testing, the characterization of cluster structure in community detection, and understanding latent space geometry. This paper concludes with a discussion of open problems and ongoing research activities addressing asymptotic normality and its implications for statistical network modeling.
The Finite Points Approximation to the PDE Problems inMulti-Asset Options

In this paper we present a meshless collocation method based on the moving least squares (MLS) approximation for numerical solution of the multiasset (d-dimensional) American option in financial mathematics. This problem is modeled by the Black-Scholes equation with moving boundary conditions. A penalty approach is applied to convert the original problem to one in a fixed domain. In finite parts, boundary conditions satisfy in associated (d−1)-dimensional Black-Scholes equations while in infinity they approach to zero. All equations are treated by the proposed meshless approximation method where the method of lines is employed for handling the time variable. Numerical examples for singleand two-asset options are illustrated.
Quantifying the impact of PFOA exposure on B-cell development and antibody production

Abstract Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals; the vast majority are environmentally and biologically persistent, and some have demonstrated toxicity, including cancer, effects on metabolism, endocrine disruption, and immune dysfunction. Suppression of T-cell-dependent antibody responses (TDAR) has been observed in numerous studies of PFAS but mechanisms remain elusive. Evidence from our work suggests that B cells and how they use energy are impacted by PFAS exposure. We hypothesize that a well-studied and immunotoxic PFAS, perfluorooctanoic acid (PFOA), alters B-cell subclasses and markers of their metabolism. Adult male and female C57BL/6 mice were given PFOA (0 or 7.5 mg/kg) via gavage for 15 days, a duration and dose sufficient to suppress the TDAR. After dosing and immunization of subgroups, spleens were prepared to quantify B-cell subsets. Flow cytometric analysis revealed decreased numbers of plasmablasts, follicular, naïve, and overall B-cell subclasses in female PFOA-exposed groups. Male PFOA-exposed groups had a significant increase in follicular B cells and other subsets had decreases, including in the overall number of B cells. Twenty-four hours after naïve B-cell isolation and ex vivo activation, metabolic measurements revealed a 5-fold increase in metabolic markers in response to stimulation in PFOA-exposed groups compared with controls. These findings suggest that B-cell development and survival may be hindered by PFOA exposure, but that activation of the remaining B cells was not. Based on these findings, PFOA-mediated suppression of the primary IgM antibody response results changes to specific subsets of B cells.
Tracheal Narrowing and Its Impact on Anesthesia Care in Patients With Morquio A (Mucopolysaccharidosis Type IVA): An Observational Study

BACKGROUND: Recently, tracheal narrowing has been recognized as a significant comorbid condition in patients with Morquio A, also known as mucopolysaccharidosis IVA. We studied a large cohort of patients with Morquio A to describe the extent of their tracheal narrowing and its relationship to airway management during anesthesia care. METHODS: This is an observational study, collecting data retrospectively, of a cohort of patients with Morquio A. Ninety-two patients with Morquio A syndrome were enrolled, among whom 44 patients had their airway evaluated by computed tomography angiography and had undergone an anesthetic within a year of the evaluation. Our hypothesis was that the tracheal narrowing as evaluated by computed tomography angiography increases with age in patients with Morquio A. The primary aim of the study was to examine the degree of tracheal narrowing in patients with Morquio A and describe the difficulties encountered during airway management, thus increasing awareness of both the tracheal narrowing and airway management difficulties in this patient population. In addition, the degree of tracheal narrowing was evaluated for its association with age or spirometry parameters using Spearman’s rank correlation. Analysis of variance followed by the Bonferroni test was used to further examine the age-based differences in tracheal narrowing for the 3 age groups: 1 to 10 years, 11 to 20 years, and >21 years. RESULTS: Patient age showed a positive correlation with tracheal narrowing (rs = 0.415; 95% confidence interval [95% CI], 0.138–0.691; P = .005) with older patients having greater narrowing of the trachea. Among spirometry parameters, FEF25%–75% showed an inverse correlation with tracheal narrowing as follows: FEF25%–75% versus tracheal narrowing: (rs = −0.467; 95% CI, −0.877 to −0.057; P = .007). During anesthetic care, significant airway management difficulties were encountered, including cancelation of surgical procedures, awake intubation using flexible bronchoscope, and failed video laryngoscopy attempts. CONCLUSIONS: Clinically significant tracheal narrowing was present in patients with Morquio A, and the degree of such narrowing likely contributed to the difficulty with airway management during their anesthetic care. Tracheal narrowing worsens with age, but the progression appears to slow down after 20 years of age. In addition to tracheal narrowing, spirometry values of FEF25%–75% may be helpful in the overall evaluation of the airway in patients with Morquio A.
α-cell electrophysiology and the regulation of glucagon secretion

Glucagon is the principal glucose-elevating hormone that forms the first-line defence against hypoglycaemia. Along with insulin, glucagon also plays a key role in maintaining systemic glucose homeostasis. The cells that secrete glucagon, pancreatic α-cells, are electrically excitable cells and use electrical activity to couple its hormone secretion to changes in ambient glucose levels. Exactly how glucose regulates α-cells has been a topic of debate for decades but it is clear that electrical signals generated by the cells play an important role in glucagon secretory response. Decades of studies have already revealed the key players involved in the generation of these electrical signals and possible mechanisms controlling them to tune glucagon release. This has offered the opportunity to fully understand the enigmatic α-cell physiology. In this review, we describe the current knowledge on cellular electrophysiology and factors regulating excitability, glucose sensing, and glucagon secretion. We also discuss α-cell pathophysiology and the perspective of addressing glucagon secretory defects in diabetes for developing better diabetes treatment, which bears the hope of eliminating hypoglycaemia as a clinical problem in diabetes care.