Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Optimal Behavior Through Reasoning and Experiences

We develop a novel framework of bounded rationality under cognitive frictions that studies learning over optimal behavior through both deliberative reasoning and accumulated experiences. Using both types of information, agents engage in Bayesian non-parametric estimation of the unknown action value function. Reasoning signals are produced internally through mental deliberation, subject to a cognitive cost. Experience signals are the observed utility outcomes at previous actions. Agents' subjective estimation uncertainty, which evolves through information accumulation, modulates the two modes of learning in a state- and history-dependent way. We discuss how the model draws on and bridges conceptual, methodological and empirical insights from both economics and the cognitive sciences literature on reinforcement learning.
Towards Image Ambient Lighting Normalization

Lighting normalization is a crucial but underexplored restoration task with broad applications. However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones. Although promising, such simplifications hinder generalizability to more realistic settings encountered in daily use. In this paper, we propose a new challenging task termed Ambient Lighting Normalization (ALN), which enables the study of interactions between shadows, unifying image restoration and shadow removal in a broader context. To address the lack of appropriate datasets for ALN, we introduce the large-scale high-resolution dataset Ambient6K, comprising samples obtained from multiple light sources and including self-shadows resulting from complex geometries, which is the first of its kind. For benchmarking, we select various mainstream methods and rigorously evaluate them on Ambient6K. Additionally, we propose IFBlend, a novel strong baseline that maximizes Image-Frequency joint entropy to selectively restore local areas under different lighting conditions, without relying on shadow localization priors. Experiments show that IFBlend achieves SOTA scores on Ambient6K and exhibits competitive performance on conventional shadow removal benchmarks compared to shadow-specific models with mask priors. The dataset, benchmark, and code are available at https://github.com/fvasluianu97/IFBlend.
On double-layer and reverse discharge creation during long positive voltage pulses in a bipolar HiPIMS discharge

Time-resolved Langmuir probe diagnostics at the discharge centerline and at three distances from the target (35mm, 60mm, and 100mm) was carried out during long positive voltage pulses (a duration of 500$\mu$s and a preset positive voltage of 100V) in bipolar High-Power Impulse Magnetron Sputtering of a Ti target (a diameter of 100mm) using an unbalanced magnetron. Fast-camera spectroscopy imaging recorded light emission from Ar and Ti atoms and singly charged ions during positive voltage pulses. It was found that during the long positive voltage pulse, the floating and the plasma potentials suddenly decrease, which is accompanied by the presence of anode light located on the discharge centerline between the target center and the magnetic null of the magnetron's magnetic field. These light patterns are related to the ignition of a reverse discharge, which leads to the subsequent rise in the plasma and the floating potentials. The reversed discharge is burning up to the end of the positive voltage pulse, but the plasma and floating potentials have lower values than the values from the initial part of the positive voltage pulse. Secondary electron emission induced by the impinging Ar$^+$ ions to the grounded surfaces in the vicinity of the discharge plasma together with the mirror configuration of the magnetron magnetic field are identified as the probable causes of the charge double-layer structure formation in front of the target and the ignition of the reverse discharge.
An Overgrowth Disorder Associated with Excessive Production of cGMP Due to a Gain-of-Function Mutation of the Natriuretic Peptide Receptor 2 Gene

We describe a three-generation family with tall stature, scoliosis and macrodactyly of the great toes and a heterozygous p.Val883Met mutation in Npr2, the gene that encodes the CNP receptor NPR2 (natriuretic peptide receptor 2). When expressed in HEK293A cells, the mutant Npr2 cDNA generated intracellular cGMP (cyclic guanosine monophosphate) in the absence of CNP ligand. In the presence of CNP, cGMP production was greater in cells that had been transfected with the mutant Npr2 cDNA compared to wild-type cDNA. Transgenic mice in which the mutant Npr2 was expressed in chondrocytes driven by the promoter and intronic enhancer of the Col11a2 gene exhibited an enhanced production of cGMP in cartilage, leading to a similar phenotype to that observed in the patients. In addition, blood cGMP concentrations were elevated in the patients. These results indicate that p.Val883Met is a constitutive active gain-of-function mutation and elevated levels of cGMP in growth plates lead to the elongation of long bones. Our findings reveal a critical role for NPR2 in skeletal growth in both humans and mice, and may provide a potential target for prevention and treatment of diseases caused by impaired production of cGMP.
Workflow Mini-Apps: Portable, Scalable, Tunable&Faithful Representations of Scientific Workflows

Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make performance reproducibility hard. In this paper, we propose workflow mini-apps as a tool to address the challenges in building and testing workflows while controlling the fidelity of representing realworld workflows. Workflow mini-apps are deployed and run on various HPC systems and architectures without workflow-specific constraints. We offer insight into their design and implementation, providing an analysis of their performance and reproducibility. Workflow mini-apps thus advance the science of workflows by providing simple, portable, and managed (fidelity) representations of otherwise complex and difficult-to-control real workflows.
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications

Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal contrastive learning techniques to ICU data, specifically focusing on clinical notes and time-series for clinically relevant online prediction tasks. We introduce a loss function Multi-Modal Neighborhood Contrastive Loss (MM-NCL), a soft neighborhood function, and showcase the excellent linear probe and zero-shot performance of our approach.
Force generation by a cylindrical cell under stationary osmolytes synthesis

Turgor is the driving force of plant growth, making possible for roots to overcome soil resistance or for stems to counteract gravity. Maintaining a constant growth rate while avoiding the cell content dilution, which would progressively stop the inward water flux, imposes the production or import of osmolytes in proportion to the increase of volume. We coin this phenomenon stationary osmoregulation. The article explores the quantitative consequences of this hypothesis on the interaction of a cylindrical cell growing axially against an obstacle. An instantaneous axial compression of a pressurized cylindrical cell generates a force and a pressure jump which both decrease toward a lower value once water has flowed out of the cell to reach the water potential equilibrium. In a first part, the article derives analytical formula for these force and over-pressure both before and after relaxation. In a second part, we describe how the coupling of the Lockhart's growth law with the stationary osmoregulation hypothesis predicts a transient slowdown in growth due to contact before a re-acceleration in growth. We finally compare these predictions with the output of an elastic growth model which ignores the osmotic origin of growth: models only match in the early phase of contact for high stiffness obstacle.
Semi-supervised Zero-Shot Learning by a Clustering-based Approach

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. In this paper, we propose a novel semi-supervised zero-shot learning method that works on an embedding space corresponding to abstract deep visual features. We seek a linear transformation on signatures to map them onto the visual features, such that the mapped signatures of the seen classes are close to labeled samples of the corresponding classes and unlabeled data are also close to the mapped signatures of one of the unseen classes. We use the idea that the rich deep visual features provide a representation space in which samples of each class are usually condensed in a cluster. The effectiveness of the proposed method is demonstrated through extensive experiments on four public benchmarks improving the state-of-the-art prediction accuracy on three of them.
Lattice study of disordering of inhomogeneous condensates and the Quantum Pion Liquid in effective $O(N)$ model

In this talk, we study a scalar $O(N)$ model with a so-called moat regime -- a regime with negative bosonic wave function renormalization -- using lattice field theory. For negative bare wave function renormalization, inhomogeneous condensates are solutions of the classical equations of motions. Using hybrid Monte Carlo simulations we demonstrate how bosonic quantum fluctuations disorder the inhomogeneous condensate. Instead, one finds a so-called Quantum Pion Liquid, where bosonic correlation functions are spatially oscillating, but also exponentially decaying.
Sexual dimorphic abnormalities in white matter geometry common to schizophrenia and non‐psychotic high‐risk subjects: Evidence for a neurodevelopmental risk marker?

The characterization of neurodevelopmental aspects of brain alterations require neuroimaging methods that reflect correlates of neurodevelopment, while being robust to other progressive pathological processes. Newly developed neuroimaging methods for measuring geometrical features of the white matter fall exactly into this category. Our recent work shows that such features, measured in the anterior corpus callosum in diffusion MRI data, correlate with psychosis symptoms in patients with adolescent onset schizophrenia and subside a reversal of normal sexual dimorphism. Here, we test the hypothesis that similar developmental deviations will also be present in nonpsychotic subjects at familial high risk (FHR) for schizophrenia, due to genetic predispositions. Demonstrating such changes would provide a strong indication of neurodevelopmental deviation extant before, and independent of pathological changes occurring after disease onset. We examined the macrostructural geometry of corpus callosum white matter in diffusion MRI data of 35 non‐psychotic subjects with genetic (familial) risk for schizophrenia, and 26 control subjects, both male and female. We report a reversal of normal sexual dimorphism in callosal white matter geometry consistent with recent results in adolescent onset schizophrenia. This pattern may be indicative of an error in neurogenesis and a possible trait marker of schizophrenia. Hum Brain Mapp 37:254–261, 2016. © 2015 Wiley Periodicals, Inc.
Analytical computation of bifurcation of orbits near collinear libration point in the restricted three-body problem

A unified analytical solution is presented for constructing the phase space near collinear libration points in the Circular Restricted Three-body Problem (CRTBP), encompassing Lissajous orbits and quasihalo orbits, their invariant manifolds, as well as transit and non-transit orbits. Traditional methods could only derive separate analytical solutions for the invariant manifolds of Lissajous orbits and halo orbits, falling short for the invariant manifolds of quasihalo orbits. By introducing a coupling coefficient {\eta} and a bifurcation equation, a unified series solution for these orbits is systematically developed using a coupling-induced bifurcation mechanism and Lindstedt-Poincar\'e method. Analyzing the third-order bifurcation equation reveals bifurcation conditions for halo orbits, quasihalo orbits, and their invariant manifolds. Furthermore, new families of periodic orbits similar to halo orbits are discovered, and non-periodic/quasi-periodic orbits, such as transit orbits and non-transit orbits, are found to undergo bifurcations. When {\eta} = 0, the series solution describes Lissajous orbits and their invariant manifolds, transit, and non-transit orbits. As {\eta} varies from zero to non-zero values, the solution seamlessly transitions to describe quasihalo orbits and their invariant manifolds, as well as newly bifurcated transit and non-transit orbits. This unified analytical framework provides a more comprehensive understanding of the complex phase space structures near collinear libration points in the CRTBP.
Efficient Multi-Band Temporal Video Filter for Reducing Human-Robot Interaction

Although mobile robots have on-board sensors to perform navigation, their efficiency in completing paths can be enhanced by planning to avoid human interaction. Infrastructure cameras can capture human activity continuously for the purpose of compiling activity analytics to choose efficient times and routes. We describe a cascade temporal filtering method to efficiently extract short- and long-term activity in two time dimensions, isochronal and chronological, for use in global path planning and local navigation respectively. The temporal filter has application either independently, or, if object recognition is also required, it can be used as a pre-filter to perform activity-gating of the more computationally expensive neural network processing. For a testbed 32-camera network, we show how this hybrid approach can achieve over 8 times improvement in frames per second throughput and 6.5 times reduction of system power use. We also show how the cost map of static objects in the ROS robot software development framework is augmented with dynamic regions determined from the temporal filter.
In Vitro Degradation of Endothelial Catenins by a Neutrophil Protease

It has been recently proposed that adhesion of polymorphonuclear cells (PMNs) to human umbilical vein endothelial cells leads to the disorganization of the vascular endothelial cadherin–dependent endothelial adherens junctions. Combined immunofluorescence and biochemical data suggested that after adhesion of PMNs to the endothelial cell surface, β-catenin, as well as plakoglobin was lost from the cadherin/catenin complex and from total cell lysates. In this study we present data that strongly suggest that the adhesion-dependent disappearance of endothelial catenins is not mediated by a leukocyte to endothelium signaling event, but is due to the activity of a neutrophil protease that is released upon detergent lysis of the cells.
Paths to Equilibrium in Normal-Form Games

In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning, where an agent who is best responding in period $t$ does not switch its strategy in the next period $t+1$. This constraint merely requires that optimizing agents do not switch strategies, but does not constrain the other non-optimizing agents in any way, and thus allows for exploration. Sequences with this property are called satisficing paths, and arise naturally in many MARL algorithms. A fundamental question about strategic dynamics is such: for a given game and initial strategy profile, is it always possible to construct a satisficing path that terminates at an equilibrium strategy? The resolution of this question has implications about the capabilities or limitations of a class of MARL algorithms. We answer this question in the affirmative for mixed extensions of finite normal-form games. %
News and Business Cycles in Open Economies

Aggregate and sectoral comovement are central features of business cycle data. Therefore, the ability to generate comovement is a natural litmus test for macroeconomic models. But it is a test that most existing models fail. In this paper we propose a unified model that generates both aggregate and sectoral comovement in response to contemporaneous shocks and news shocks about fundamentals. The fundamentals that we consider are aggregate and sectoral TFP shocks as well as investment-specific technical change. The model has three key elements: variable capital utilization, adjustment costs to investment, and a new form of preferences that allow us to parameterize the strength of short-run wealth effects on the labor supply.
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning

Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning rather than complete retraining. This poses a challenge in striking a balance between stability and plasticity when adapting to new information. In this paper, we employ Centered Kernel Alignment for quantitatively analyzing model stability and plasticity, revealing the critical roles of batch normalization layers for stability and convolutional layers for plasticity. Motivated by this, we propose Branch-tuning, an efficient and straightforward method that achieves a balance between stability and plasticity in continual SSL. Branch-tuning consists of branch expansion and compression, and can be easily applied to various SSL methods without the need of modifying the original methods, retaining old data or models. We validate our method through incremental experiments on various benchmark datasets, demonstrating its effectiveness and practical value in real-world scenarios. We hope our work offers new insights for future continual self-supervised learning research. The code will be made publicly available.
Channel Estimation and Beamforming for Beyond Diagonal Reconfigurable Intelligent Surfaces

Beyond diagonal reconfigurable intelligent surface (BD-RIS) is a new advance and generalization of the RIS technique. BD-RIS breaks through the isolation between RIS elements by creatively introducing inter-element connections, thereby enabling smarter wave manipulation and enlarging coverage. However, exploring proper channel estimation schemes suitable for BD-RIS aided communication systems still remains an open problem. In this paper, we study channel estimation and beamforming design for BD-RIS aided multi-antenna systems. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimation, and formulate the joint pilot sequence and BD-RIS design problem with unique constraints induced by BD-RIS architectures. Specifically, we propose an efficient pilot sequence and BD-RIS design which theoretically guarantees to achieve the minimum MSE. With the estimated channel, we then consider two BD-RIS scenarios and propose beamforming design algorithms. Finally, we provide simulation results to verify the effectiveness of the proposed channel estimation scheme and beamforming design algorithms. We also show that more interelement connections in BD-RIS improves the performance while increasing the training overhead for channel estimation.
Evaporation from (Blue-)Green Roofs: Assessing the Benefits of a Storage and Capillary Irrigation System Based on Measurements and Modeling

Worldwide cities are facing increasing temperatures due to climate change and increasing urban density. Green roofs are promoted as a climate adaptation measure to lower air temperatures and improve comfort in urban areas, especially during intensive dry and warm spells. However, there is much debate on the effectiveness of this measure, because of a lack of fundamental knowledge about evaporation from different green roof systems. In this study, we investigate the water and energy balance of different roof types on a rooftop in Amsterdam, the Netherlands. Based on lysimeter measurements and modeling, we compared the water and energy balance of a conventional green roof with blue-green roofs equipped with a novel storage and capillary irrigation system. The roofs were covered either with Sedum or by grasses and herbs. Our measurements and modeling showed that conventional green roof systems (i.e., a Sedum cover and a few centimeters of substrate) have a low evaporation rate and due to a rapid decline in available moisture, a minor cooling effect. Roofs equipped with a storage and capillary irrigation system showed a remarkably large evaporation rate for Sedum species behaving as C3 plants during hot, dry periods. Covered with grasses and herbs, the evaporation rate was even larger. Precipitation storage and capillary irrigation strongly reduced the number of days with dry-out events. Implementing these systems therefore could lead to better cooling efficiencies in cities.
A framework for user interface design in visual information retrieval

This paper describes the user interface framework of the VizIR project (Eidenberger et al., 2002). VizIR is an open project to develop a Java-based, extendible and well-documented asset framework for visual information retrieval. The paper includes a description of the visual components and their class structure, the communication between panels as well as between visual components and query engines. Visual components include media panels, controls and renderer classes. The panels communicate through media events. Communication of user interfaces and query engines is based on the multimedia retrieval markup language (MRML). MRML is an XML-based language that was developed by the University of Geneva. To be usable with our querying paradigm, we extend MRML with additional elements. The paper contains a short implementation section with details on the Java components used, Java 3D graphics libraries (GLAJava and Java3D) and Java XML parsing. Finally, an appendix is included containing the MRML extension.
Nijenhuis deformations of Poisson algebras and $F$-manifold algebras

The notion of pre-Poisson algebras was introduced by Aguiar in his study of zinbiel algebras and pre-Lie algebras. In this paper, we first introduce NS-Poisson algebras as a generalization of both Poisson algebras and pre-Poisson algebras. An NS-Poisson algebra has an associated sub-adjacent Poisson algebra. We show that a Nijenhuis operator on a Poisson algebra deforms the structure into an NS-Poisson algebra. The semi-classical limit of an NS-algebra deformation and a suitable filtration of an NS-algebra produce NS-Poisson algebras. On the other hand, $F$-manifold algebras were introduced by Dotsenko as the underlying algebraic structure of $F$-manifolds. We also introduce NS-$F$-manifold algebras as a simultaneous generalization of NS-Poisson algebras, $F$-manifold algebras and pre-$F$-manifold algebras. In the end, we show that Nijenhuis deformations of $F$-manifold algebras and the semi-classical limits of NS-pre-Lie algebra deformations have NS-$F$-manifold algebra structures.