Miracle-dz/UniFAR-FTUs huggingface dataset:
On a parametrization of spaces of maximal framed representations

This article is an extended version of the talk given by the author in the seminar Th\'eorie Spectrale et G\'eom\'etrie at the Institut Fourier in March 2022. We present some results from the author's doctoral thesis, extended by several results from other papers. We give a parametrization of the space of maximal framed representations of the fundamental group of a punctured surface into a Hermitian Lie group of tube type that can be seen as $\mathrm{Sp}_2(A,\sigma)$ for a Hermitian algebra $(A,\sigma)$. Using this parametrization, we count connected components of the space of maximal framed representations as well as the space of maximal (non-framed) representations.
A novel Multi to Single Module for small object detection

Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features. This issue stems from information misalignment caused by variations in feature scale and information loss during feature processing. In response to this challenge, this paper proposes a novel the Multi to Single Module (M2S), which enhances a specific layer through improving feature extraction and refining features. Specifically, M2S includes the proposed Cross-scale Aggregation Module (CAM) and explored Dual Relationship Module (DRM) to improve information extraction capabilities and feature refinement effects. Moreover, this paper enhances the accuracy of small object detection by utilizing M2S to generate an additional detection head. The effectiveness of the proposed method is evaluated on two datasets, VisDrone2021-DET and SeaDronesSeeV2. The experimental results demonstrate its improved performance compared with existing methods. Compared to the baseline model (YOLOv5s), M2S improves the accuracy by about 1.1\% on the VisDrone2021-DET testing dataset and 15.68\% on the SeaDronesSeeV2 validation set.
Semi-Simplicial Set Models for Distributed Knowledge

In recent years, a new class of models for multi-agent epistemic logic has emerged, based on simplicial complexes. Since then, many variants of these simplicial models have been investigated, giving rise to different logics and axiomatizations. In this paper, we present a further generalization, which encompasses all previously studied variants of simplicial models. Geometrically, this is achieved by generalizing beyond simplicial complexes, and considering instead semi-simplicial sets. By doing so, we define a new semantics for epistemic logic with distributed knowledge, where a group of agents may distinguish two worlds, even though each individual agent in the group is unable to distinguish them. As it turns out, these models are the geometric counterpart of a generalization of Kripke models, called "pseudo-models". We show how to recover the previously defined variants of simplicial models as sub-classes of our models; and give a sound and complete axiomatization for each of them.
Detection and Quantification of Eurycomanone Levels in Tongkat Ali Herbal Products

Tongkat Ali is most famous for its aphrodisiac property and has gained reputation worldwide. Due to this, Tongkat Ali products are in demand and can be obtained in various forms. The quality of these products is not often authenticated. As Malaysia is one of the major producing countries of Tongkat Ali products, this study adapted a method which is used to determine the quality of Tongkat Ali products in the market. The quality of those products is based on the level of eurycomanone, the major compound in Tongkat Ali and the level should be around 0.8-1.5 w/v (%). A total of 41 products were obtained internationally and from Malaysia, with different pharmaceutical dosage, single or combined formulation. Those products were analyzed for eurycomanone content using HPLC, C18 column with acetonitrile and water as the mobile phase. The result indicated that 24 of the products contained eurycomanone. Eleven out of the 24 products complied (0.84-8.48% w/v) with the criteria set by Malaysian Standard and 9 of the products even have the eurycomanone level above (1.6-8.48% w/v) the set criteria. It was observed no urycomanone was detected in some products even though the products have been registered with Malaysian herbal regulatory body, National Pharmaceutical Control Bureau (NPCB). In conclusion, this study suggests that the eurycomanone level should be utilized as a mandatory regulatory parameter for Tongkat Ali herbal preparation in the market besides the current parameters; microbial load and heavy metal contents alone
Scattering Parameter Measurements of the Long Wavelength Array Antenna and Front End Electronics

We present recent 2-port vector network analyzer (VNA) measurements of the complete set of scattering parameters for the antenna used within the Long Wavelength Array (LWA) and the associated front end electronics (FEEs). Full scattering parameter measurements of the antenna yield not only the reflection coefficient for each polarization, S11 and S22, but also the coupling between polarizations, S12 and S21. These had been previously modeled using simulations, but direct measurements had not been obtained until now. The measurements are used to derive a frequency dependent impedance mismatch factor (IMF) which represents the fraction of power that is passed through the antenna–FEE interface and not reflected due to a mismatch between the impedance of the antenna and the impedance of the FEE. We also present results from a two-antenna experiment where each antenna is hooked up to a separate port on the VNA. This allows for cross–antenna coupling to be measured for all four possible polarization combinations. Finally, we apply the newly measured IMF and FEE forward gain corrections to LWA data to investigate how well they remove instrumental effects.
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching

Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that can learn any dense task from a few labeled images is desired. Yet, current few-shot learning methods target a restricted set of tasks such as semantic segmentation, presumably due to challenges in designing a general and unified model that is able to flexibly and efficiently adapt to arbitrary tasks of unseen semantics. We propose Visual Token Matching (VTM), a universal few-shot learner for arbitrary dense prediction tasks. It employs non-parametric matching on patch-level embedded tokens of images and labels that encapsulates all tasks. Also, VTM flexibly adapts to any task with a tiny amount of task-specific parameters that modulate the matching algorithm. We implement VTM as a powerful hierarchical encoder-decoder architecture involving ViT backbones where token matching is performed at multiple feature hierarchies. We experiment VTM on a challenging variant of Taskonomy dataset and observe that it robustly few-shot learns various unseen dense prediction tasks. Surprisingly, it is competitive with fully supervised baselines using only 10 labeled examples of novel tasks (0.004% of full supervision) and sometimes outperforms using 0.1% of full supervision. Codes are available at https://github.com/GitGyun/visual_token_matching.
Complex potential formulation of the axially symmetric gravitational field problem

Spin‐coefficients and null tetrad components of the Ricci tensor and the Weyl conform tensor are evaluated in terms of a single complex gravitational potential e, while null tetrad components of the electromagnetic stress energy tensor are evaluated in terms of a second complex potential φ. All the results are expressed elegantly in terms of a differential operator ð, similar to the ``thop'' of Newman and Penrose. The problem of finding physically pertinent stationary axially symmetric Einstein‐Maxwell fields is reduced to the search for a complex solution ξ0(x, y) of one nonlinear differential equation subject to simple subsidiary conditions.
An Extensive Catalog of Early-type Dwarf Galaxies in the Local Universe: Morphology and Environment

We present an extensive catalog of 5405 early-type dwarf (dE) galaxies located in the various environments, i.e., clusters, groups, and fields, of the local universe (z < 0.01). The dEs are selected through visual inspection of the Legacy survey’s g–r–z combined tricolor images. The inspected area, covering a total sky area of 7643 deg2, encompasses two local clusters (Virgo and Fornax), 265 groups, and the regions around 586 field galaxies of M K < −21 mag. The catalog aims to be one of the most extensive and publicly accessible collections of data on dEs, despite its complex completeness limits, which may not accurately represent its statistical completeness. The strength of the catalog lies in the morphological characteristics, including nucleated, tidal, and ultradiffuse dEs. The two clusters contribute nearly half (2437 out of 5405) of the dEs, and the 265 groups contribute 2103 dEs. There are 864 dEs in 586 fields, i.e., ∼1.47 dEs per field. Using a standard definition commonly used in literature, we identify 100 ultradiffuse galaxies (UDGs), which compose ∼2% of the dE population. We find that 40% of our sample dEs harbor a central nucleus, and among the UDG population, a majority (79%) are nonnucleated. About 1.3% of dEs suffer from ongoing tidal disturbance by nearby massive galaxies, and only 0.03% show a sign of recent dwarf–dwarf mergers. The association between dEs and their nearest bright neighbor galaxies suggests that dEs are more likely created where their neighbors are non-star-forming ones.
Fredman’s Trick Meets Dominance Product: Fine-Grained Complexity of Unweighted APSP, 3SUM Counting, and More

In this paper we carefully combine Fredman’s trick [SICOMP’76] and Matoušek’s approach for dominance product [IPL’91] to obtain powerful results in fine-grained complexity. Under the hypothesis that APSP for undirected graphs with edge weights in {1, 2, …, n} requires n3−o(1) time (when ω=2), we show a variety of conditional lower bounds, including an n7/3−o(1) lower bound for unweighted directed APSP and an n2.2−o(1) lower bound for computing the Minimum Witness Product between two n × n Boolean matrices, even if ω=2, improving upon their trivial n2 lower bounds. Our techniques can also be used to reduce the unweighted directed APSP problem to other problems. In particular, we show that (when ω = 2), if unweighted directed APSP requires n2.5−o(1) time, then Minimum Witness Product requires n7/3−o(1) time. We show that, surprisingly, many central problems in fine-grained complexity are equivalent to their natural counting versions. In particular, we show that Min-Plus Product and Exact Triangle are subcubically equivalent to their counting versions, and 3SUM is subquadratically equivalent to its counting version. We also obtain new algorithms using new variants of the Balog-Szemerédi-Gowers theorem from additive combinatorics. For example, we get an O(n3.83) time deterministic algorithm for exactly counting the number of shortest paths in an arbitrary weighted graph, improving the textbook O(n4) time algorithm. We also get faster algorithms for 3SUM in preprocessed universes, and deterministic algorithms for 3SUM on monotone sets in {1, 2, …, n}d.
Learning Versatile 3D Shape Generation with Improved AR Models

Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the 3D domain for powerful shape generation, it still has two limitations: expensive computations on volumetric grids and ambiguous auto-regressive order along grid dimensions. To overcome these limitations, we propose the Improved Auto-regressive Model (ImAM) for 3D shape generation, which applies discrete representation learning based on a latent vector instead of volumetric grids. Our approach not only reduces computational costs but also preserves essential geometric details by learning the joint distribution in a more tractable order. Moreover, thanks to the simplicity of our model architecture, we can naturally extend it from unconditional to conditional generation by concatenating various conditioning inputs, such as point clouds, categories, images, and texts. Extensive experiments demonstrate that ImAM can synthesize diverse and faithful shapes of multiple categories, achieving state-of-the-art performance.
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection

Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in both base and novel classes. Additionally, our novel RoI-based masked attention and RoI pruning techniques help leverage the zero-shot classification ability of the Vision Transformer-based CLIP, resulting in improved detection performance at minimal computational cost. Our experiments on the OV-COCO and OVLVIS datasets demonstrate that Prompt-OVD achieves an impressive 21.2 times faster inference speed than the first end-to-end open-vocabulary detection method (OV-DETR), while also achieving higher APs than four two-stage-based methods operating within similar inference time ranges. Code will be made available soon.
Selective Structured State-Spaces for Long-Form Video Understanding

Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence ($S4$) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all imagetokens equally as done by $S4$ model can adversely affect its efficiency and accuracy. To address this limitation, we present a novel Selective $S4$ (i.e., $S5)$ model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos. Unlike previous mask-based token reduction methods used in transformers, our $S5$ model avoids the dense self-attention calculation by making use of the guidance of the momentum-updated $S4$ model. This enables our model to efficiently discard less informative tokens and adapt to various long-form video understanding tasks more effectively. However, as is the case for most token reduction methods, the informative image tokens could be dropped incorrectly. To improve the robustness and the temporal horizon of our model, we propose a novel long-short masked contrastive learning (LSMCL) approach that enables our model to predict longer temporal context using shorter input videos. We present extensive comparative results using three challenging long-form video understanding datasets (LVU, COIN and Breakfast), demonstrating that our approach consistently outperforms the previous state-of-the-art S4 model by up to 9.6% accuracy while reducing its memory footprint by 23%.
Green Federated Learning

The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint. Federated Learning (FL) - a collaborative machine learning technique for training a centralized model using data of decentralized entities - can also be resource-intensive and have a significant carbon footprint, particularly when deployed at scale. Unlike centralized AI that can reliably tap into renewables at strategically placed data centers, cross-device FL may leverage as many as hundreds of millions of globally distributed end-user devices with diverse energy sources. Green AI is a novel and important research area where carbon footprint is regarded as an evaluation criterion for AI, alongside accuracy, convergence speed, and other metrics. In this paper, we propose the concept of Green FL, which involves optimizing FL parameters and making design choices to minimize carbon emissions consistent with competitive performance and training time. The contributions of this work are two-fold. First, we adopt a data-driven approach to quantify the carbon emissions of FL by directly measuring real-world at-scale FL tasks running on millions of phones. Second, we present challenges, guidelines, and lessons learned from studying the trade-off between energy efficiency, performance, and time-to-train in a production FL system. Our findings offer valuable insights into how FL can reduce its carbon footprint, and they provide a foundation for future research in the area of Green AI.
Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma,as well as related histology and molecular markers on a multi-institutional dataset.
Proximal bundle methods for hybrid weakly convex composite optimization problems

This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update scheme which includes various well-known bundle update schemes. In contrast to other wellknown stationary conditions in the context of HWC-CO, PBF uses a new stationarity measure which is easily verifiable and, at the same time, implies any of the former ones.
Curricular Contrastive Regularization for Physics-Aware Single Image Dehazing

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C2PNet. Extensive experiments demonstrate that our C2PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. Code is available at https://github.com/YuZheng9/C2PNet.
Pedestrian Behavior Interacting with Autonomous Vehicles: Role of AV Operation and Signal Indication and Roadway Infrastructure

Interacting with pedestrians is challenging for Autonomous vehicles (AVs). This study evaluates how AV operations /associated signaling and roadway infrastructure affect pedestrian behavior in virtual reality. AVs were designed with different operations and signal indications, including negotiating with no signal, negotiating with a yellow signal, and yellow/blue negotiating/no-yield indications. Results show that AV signal significantly impacts pedestrians' accepted gap, walking time, and waiting time. Pedestrians chose the largest open gap between cars with AV showing no signal, and had the slowest crossing speed with AV showing a yellow signal indication. Roadway infrastructure affects pedestrian walking time and waiting time.
Random measure priors in Bayesian frequency recovery from sketches

Given a lossy-compressed representation, or sketch, of data with values in a set of symbols, the frequency recovery problem considers the estimation of the empirical frequency of a new data point. Recent studies have applied Bayesian nonparametrics (BNPs) to develop learning-augmented versions of the popular count-min sketch (CMS) recovery algorithm. In this paper, we present a novel BNP approach to frequency recovery, which is not built from the CMS but still relies on a sketch obtained by random hashing. Assuming data to be modeled as random samples from an unknown discrete distribution, which is endowed with a Poisson-Kingman (PK) prior, we provide the posterior distribution of the empirical frequency of a symbol, given the sketch. Estimates are then obtained as mean functionals. An application of our result is presented for the Dirichlet process (DP) and Pitman-Yor process (PYP) priors, and in particular: i) we characterize the DP prior as the sole PK prior featuring a property of sufficiency with respect to the sketch, leading to a simple posterior distribution; ii) we identify a large sample regime under which the PYP prior leads to a simple approximation of the posterior distribution. Then, we develop our BNP approach to a"traits"formulation of the frequency recovery problem, not yet studied in the CMS literature, in which data belong to more than one symbol (trait), and exhibit nonnegative integer levels of associations with each trait. In particular, by modeling data as random samples from a generalized Indian buffet process, we provide the posterior distribution of the empirical frequency level of a trait, given the sketch. This result is then applied under the assumption of a Poisson and Bernoulli distribution for the levels of associations, leading to a simple posterior distribution and a simple approximation of the posterior distribution, respectively.
Exponential decay estimates for semilinear wave-type equations with time-dependent time delay

In this paper, we analyze a semilinear damped second order evolution equation with time-dependent time delay and time-dependent delay feedback coefficient. The nonlinear term satisfies a local Lipschitz continuity assumption. Under appropriate conditions, we prove well-posedness and exponential stability of our model for small initial data. Our arguments combine a Lyapunov functional approach with some continuity arguments. Moreover, as an application of our abstract results, the damped wave equation with a source term and delay feedback is analyzed.