Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties

This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models. It is based on two core designs. First, we employ Large Language Models (LLMs) and carefully crafted prompts to generate descriptions of all involved categories that carry meaningful common sense knowledge and follow a structured format. Second, we introduce a description embedding model preserving semantic correlation across descriptions and then cluster them into a set of descriptive properties (e.g., 256) using K-Means. These properties are based on interpretable common sense knowledge consistent with theories of human recognition. We empirically show that our approach makes segmentation models perform stronger on five classic benchmarks (e.g., ADE20K, COCO-Stuff, Pascal Context, Cityscapes, and BDD). Our method also shows better scalability with extended training steps than category-level supervision. Our interpretable segmentation framework also emerges with the generalization ability to segment out-of-domain or unknown categories using only in-domain descriptive properties. Code is available at https://github.com/lambert-x/ProLab.
How to make log structures

We introduce the concept of a viable generically toroidal crossing (gtc) Deligne--Mumford stack $Y$. This generalizes the concept of Gorenstein toroidal crossing space, which in turn generalizes that of a simple normal crossing scheme. On such a space $Y$, we define by explicit construction a natural sheaf $\mathcal{LS}_Y$, intrinsic to $Y$. Our main theorem states that the set of nowhere vanishing sections $\Gamma(Y,\mathcal{LS}_Y^\times)$ is canonically bijective to the set of isomorphism classes of log structures on $Y$ over $k^\dagger$ compatible with the gtc structure. The definition of $\mathcal{LS}_Y$ by explicit construction permits the effective construction of log structures on $Y$; it also enables logarithmic birational geometry, in particular the construction -- in some cases -- of resolutions of singular log structures. Our work generalizes Theorem 3.22 in GS06 and our proof follows closely the proof of that theorem as given in GS06.
Federated Continual Novel Class Learning

In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique. However, most existing FL studies assume that the data distribution remains nearly fixed over time, while real-world scenarios often involve dynamic and continual changes. To equip FL systems with continual model evolution capabilities, we focus on an important problem called Federated Continual Novel Class Learning (FedCN) in this work. The biggest challenge in FedCN is to merge and align novel classes that are discovered and learned by different clients without compromising privacy. To address this, we propose a Global Alignment Learning (GAL) framework that can accurately estimate the global novel class number and provide effective guidance for local training from a global perspective, all while maintaining privacy protection. Specifically, GAL first locates high-density regions in the representation space through a bi-level clustering mechanism to estimate the novel class number, with which the global prototypes corresponding to novel classes can be constructed. Then, GAL uses a novel semantic weighted loss to capture all possible correlations between these prototypes and the training data for mitigating the impact of pseudo-label noise and data heterogeneity. Extensive experiments on various datasets demonstrate GAL's superior performance over state-of-the-art novel class discovery methods. In particular, GAL achieves significant improvements in novel-class performance, increasing the accuracy by 5.1% to 10.6% in the case of one novel class learning stage and by 7.8% to 17.9% in the case of two novel class learning stages, without sacrificing known-class performance. Moreover, GAL is shown to be effective in equipping a variety of different mainstream FL algorithms with novel class discovery and learning capability, highlighting its potential for many real-world applications.
Upper Bound on Thermal Gravitational Wave Backgrounds from Hidden Sectors

Hot viscous plasmas unavoidably emit a gravitational wave background, similar to the electromagnetic black body radiation. We study the contribution from hidden particles to the diffuse background emitted by the primordial plasma in the early universe. While this contribution can easily dominate over that from Standard Model particles, we find that both are capped by a generic upper bound that makes them difficult to detect with interferometers in the foreseeable future. However, resonant cavity experiments could potentially observe backgrounds that saturate the upper bound. We illustrate our results for axions and heavy neutral leptons. Finally, our results suggest that previous works overestimated the gravitational wave background from particle decays out of thermal equilibrium.
NeLF-Pro: Neural Light Field Probes

We present NeLF-Pro, a novel representation for modeling and reconstructing light fields in diverse natural scenes that vary in extend and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally, we model the light field of a scene as a set of local light field feature probes, parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e., VM) shared among local feature probes, and a basis factor (i.e., M) - efficiently encoding internal relationships and patterns within the scene.Experimentally, we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations, and achieves fast reconstruction with better rendering quality while maintaining compact modeling.
TinySAM: Pushing the Envelope for Efficient Segment Anything Model

Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pretrained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive computational capacity, which hinders the further application of SAM on computation constrained edge devices. To this end, in this paper we propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with hard prompt sampling and hard mask weighting strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reduce the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by $2\times$ with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods. Pre-trained models and codes are available at https://github.com/xinghaochen/TinySAM and https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM.
The neuron as a temporal electroacoustic medium

The human brain is one of the most complex and intriguing scientific topics. The most established theory on neuronal communication is a pure electrical model based on the propagation of intracell cationic charges along the neurons. Here we propose a complementary model based on two properties of brain communication: A) The Coulomb interaction associated to the Action Potential (AP) pulse induces a deformation of the neuron membrane which travels as an acoustic signal, i.e.: The ions play an essential role and the electric and acoustic signals, composing the AP, are strongly correlated. B) As brain communication is stablished through a periodic train of AP pulses it induces a time periodic modulation of the acoustic parameters. In this framework we propose envisaging the neuron as a temporal electro-acoustic medium. The temporal varying media framework could help understanding brain conundrums such as propagation routes involved in the neuronal plasticity in the consolidation of the memory, as well as on the generation of the signals associated to the brain field theory.
ProvFL: Client-Driven Interpretability of Global Model Predictions in Federated Learning

Federated Learning (FL) trains a collaborative machine learning model by aggregating multiple privately trained clients' models over several training rounds. Such a long, continuous action of model aggregations poses significant challenges in reasoning about the origin and composition of such a global model. Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning. FL application developers often question: (1) what clients contributed towards a global model and (2) if a global model predicts a label, which clients are responsible for it? We introduce, neuron provenance, a fine-grained lineage capturing mechanism that tracks the flow of information between the individual participating clients in FL and the final global model. We operationalize this concept in ProvFL that functions on two key principles. First, recognizing that monitoring every neuron of every client's model statically is ineffective and noisy due to the uninterpretable nature of individual neurons, ProvFL dynamically isolates influential and sensitive neurons in the global model, significantly reducing the search space. Second, as multiple clients' models are fused in each round to form a global model, tracking each client's contribution becomes challenging. ProvFL leverages the invertible nature of fusion algorithms to precisely isolate each client's contribution derived from selected neurons. When asked to localize the clients responsible for the given behavior (i.e., prediction) of the global model, ProvFL successfully localizes them with an average provenance accuracy of 97%. Additionally, ProvFL outperforms the state-of-the-art FL fault localization approach by an average margin of 50%.
Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period

We propose difference-in-differences estimators for continuous treatments with heterogeneous effects. We assume that between consecutive periods, the treatment of some units, the switchers, changes, while the treatment of other units does not change. We show that under a parallel trends assumption, an unweighted and a weighted average of the slopes of switchers' potential outcomes can be estimated. While the former parameter may be more intuitive, the latter can be used for cost-benefit analysis, and it can often be estimated more precisely. We generalize our estimators to the instrumental-variable case. We use our results to estimate the price-elasticity of gasoline consumption.
Genomic evidence for West Antarctic Ice Sheet collapse during the Last Interglacial

The marine-based West Antarctic Ice Sheet (WAIS) is considered vulnerable to irreversible collapse under future climate trajectories and its tipping point may even lie within the mitigated warming scenarios of 1.5–2 °C of the United Nations Paris Agreement. Knowledge of ice loss during similarly warm past climates, including the Last Interglacial, when global sea levels were 5–10 m higher than today, and global average temperatures of 0.5–1.5 °C warmer than preindustrial levels, could resolve this uncertainty. Here we show, using a panel of genome-wide, single nucleotide polymorphisms of a circum-Antarctic octopus, persistent, historic signals of gene flow only possible with complete WAIS collapse. Our results provide the first empirical evidence that the tipping point of WAIS loss could be reached even under stringent climate mitigation scenarios. One-Sentence Summary Historical gene flow in marine animals indicate the West Antarctic Ice Sheet collapsed during the Last Interglacial.
Highlighting health consequences of racial disparities sparks support for action

Racial disparities arise across many vital areas of American life, including employment, health, and interpersonal treatment. For example, one in three Black children lives in poverty (versus one in nine white children), and, on average, Black Americans live four fewer years compared with white Americans. Which disparity is more likely to spark reduction efforts? We find that highlighting disparities in health-related (versus economic) outcomes spurs greater social media engagement and support for disparity-mitigating policy. Further, reading about racial health disparities elicits greater support for action (e.g., protesting) compared with economic- or belonging-based disparities. This occurs in part because people view health disparities as violating morally sacred values, which enhances perceived injustice. This work elucidates which manifestations of racial inequality are most likely to prompt Americans to action. Editor’s summary Racism in America is a system of oppression that produces and sustains racially unequal outcomes across education, the economy, health, incarceration, and other institutions. These intertwined institutions place Black Americans at a compounded disadvantage even in the absence of overt discrimination. An effective way to challenge systemic racism is through social movements or policies. However, Black-White disparities in some domains (e.g., poverty and alienation) may not mobilize resistance toward racism as much as disparities in other domains (e.g., health and physical harm), although all domains are pernicious and reinforce each other. Brown et al. found that threats to health or body (e.g., aggressive police encounters) spark outrage and motivate more action because they trigger morality concerns (see the Perspective by Earl and Derricks). Framing disparities to engage moral injustice feelings may better motivate policy reform to dismantle systemic racism. —Ekeoma Uzogara Racial health and physical harm disparities violate morality concerns, motivating action.
First-episode psychosis intervention programme versus standard care for the clinical management of early phases of psychosis: cost analysis

Background Early intervention programmes (EIPs) in psychosis have gained attention as specialised interventions to improve health-related and societal impacts for people with psychotic disorders. Previous studies have presented evidence in favour of EIPs over the first year of intervention, despite none considering the critical period before psychosis onset (5 years). Aims To compare the associated costs of the First Episode Psychosis Intervention Program (CRUPEP) and treatment as usual (TAU) in a real-world cohort in a non-specialised psychiatric community setting. Method Direct and indirect mental health-related costs were calculated over 1 year and up to 7 years. Healthcare and societal costs were calculated from economic data related to the consumption of all healthcare resources, including emergency department attendances, hospital admissions, psychotropic medication prescriptions and societal costs. Results From a healthcare perspective, the intervention (CRUPEP) group initially showed a marginally higher cost per patient than the TAU group (€7621 TAU group v. €11 904 CRUPEP group) over the first year of follow-up. However, this difference was reversed between the groups on considering the entire follow-up, with the TAU group showing considerably higher associated costs per patient (€77 026 TAU v. €25 247 CRUPEP). Conclusions The EIP (CRUPEP) showed clinical benefits and minimised the direct and indirect health-related costs of the management of psychosis. Although the CRUPEP intervention initially reported increased costs over 1 year, TAU surpassed the global costs over the entire follow-up.
A New Species of Nemozoma Latreille (Coleoptera: Cleroidea: Trogossitidae) from Honduras

Abstract Nemozoma woodi Kippenhan, new species is described and illustrated from specimens collected from Zamorano, Departamento de Francisco Morazán, Honduras. This new species is compared to other, morphologically similar New World species. A key to the Nemozoma species of North and Central America is provided.
The Effects of Competitive Environment on Supply Chain Information Sharing and Performance: An Empirical Study in China

Information sharing in supply chains has become an important topic over the past decade. This study uses data from 617 Chinese manufacturing firms to investigate the relationships among competitive environments, supply chain information sharing (SCIS), and supply chain performance. The results of structural equation modeling analysis show that (i) international competition is positively related to all three types of SCIS whereas local competition is not significantly related to any of the three types, (ii) internal information sharing is positively related to external information sharing with suppliers and customers, and (iii) internal information sharing and information sharing with customers are positively related to superior supply chain performance, whereas supplier information sharing is not significantly related to performance. The findings enhance our understanding of the relationships among competitive environment, SCIS, and supply chain performance in Chinese manufacturing settings.
The molecular basis of sugar detection by an insect taste receptor

Animals crave sugars because of their energy potential and the pleasurable sensation of tasting sweetness. Yet all sugars are not metabolically equivalent, requiring mechanisms to detect and differentiate between chemically similar sweet substances. Insects use a family of ionotropic gustatory receptors to discriminate sugars, each of which is selectively activated by specific sweet molecules. To gain insight into the molecular basis of sugar selectivity, we determined structures of Gr9, a gustatory receptor from the silkworm Bombyx mori (BmGr9), in the absence and presence of its sole activating ligand, D-fructose. These structures, along with structure-guided mutagenesis and functional assays, illustrate how specificity for D-fructose is seemingly achieved by a ligand-binding pocket that precisely matches the overall shape and pattern of chemical groups in D-fructose. However, our computational docking and experimental binding assays revealed that other sugars also bind BmGr9, yet they are unable to activate the receptor. We identified the conformational change required to open the channel gate that provides an additional layer of receptor tuning in BmGr9; only D-fructose can both fit into the pocket and simultaneously engage a bridge of two conserved aromatic residues that connects the pocket to the ion conducting pore. Thus, chemical specificity does not depend solely on the selectivity of the ligand-binding pocket, but it is an emergent property arising from a combination of receptor-ligand interactions and allosteric coupling. Our results support a model whereby coarse receptor tuning is derived from the size and chemical characteristics of the pocket, whereas fine-tuning of receptor activation is achieved through the selective engagement of an allosteric pathway that regulates ion conduction.
Subregional characterization of mesoscale eddies across the Brazil-Malvinas Confluence

Horizontal and vertical motions associated with coherent mesoscale structures, including eddies and meanders, are responsible for significant global transports of many properties, including heat and mass. Mesoscale vertical fluxes also influence upper ocean biological productivity by mediating the supply of nutrients into the euphotic layer, with potential impacts on the global carbon cycle. The Brazil-Malvinas Confluence (BMC) is a western boundary current region in the South Atlantic with intense mesoscale activity. This region has an active role in the genesis and transformation of water masses and thus is a critical component of the Atlantic meridional overturning circulation. The collision between the Malvinas and Brazil Currents over the Patagonian shelf/slope creates an energetic front that translates offshore to form a vigorous eddy field. Recent improvements in gridded altimetric sea level anomaly fields allow us to track BMC mesoscale eddies with high spatial and temporal resolutions using an automated eddy tracker. We characterize the eddies across fourteen 5°×5° subregions. Eddy-centric composites of tracers and geostrophic currents diagnosed from a global reanalysis of surface and in situ data reveal substantial subregional heterogeneity. The in situ data are also used to compute the evolving quasi-geostrophic vertical velocity (QG-ω) associated with each instantaneous eddy instance. The QG- ω eddy composites have the expected dipole patterns of alternating upwelling/downwelling, however the magnitude and sign of azimuthally-averaged vertical velocity varies among subregions. Maximum eddy values are found near fronts and sharp topographic gradients. In comparison with regional eddy composites, subregional composites provide refined information about mesoscale eddy heterogeneity. This article is protected by copyright. All rights reserved.
Anp32e protects against accumulation of H2A.Z at Sox motif containing promoters during zebrafish gastrulation

Epigenetic regulation of chromatin states is crucial for proper gene expression programs and progression during development, but precise mechanisms by which epigenetic factors influence differentiation remain poorly understood. Here we find that the histone variant H2A.Z accumulates at Sox motif-containing promoters during zebrafish gastrulation while neighboring genes become transcriptionally active. These changes coincide with reduced expression of anp32e, the H2A.Z histone removal chaperone, suggesting that loss of Anp32e may lead to increases in H2A.Z during differentiation. Remarkably, genetic removal of Anp32e in embryos leads to H2A.Z accumulation prior to gastrulation, and precocious developmental transcription of Sox motif associated genes. Altogether, our results provide compelling evidence for a mechanism in which Anp32e restricts H2A.Z accumulation at Sox motif-containing promoters, and subsequent down-regulation of Anp32e enables temporal up-regulation of Sox motif associated genes. Highlights An early-developmental time course of zebrafish chromatin accessibility is achieved using an integrated UMAP analysis of datasets from two separate published studies. CUT&Tag sequencing is used to characterize the genomic localization for the histone chaperone ANP32E. Changes in Anp32e enrichment coincide with opposing changes in H2A.Z enrichment during zebrafish gastrulation. Precociously expressed genes in embryos lacking Anp32e are disproportionately Sox-marked and may represent H2A.Z-mediated developmental accelerations.