Miracle-dz/UniFAR-FTUs huggingface dataset:
The macroinvertebrate fauna of the Młynne stream (Polish Carpathians) in the aspect of the bed load transport and water quality

Abstract The macroinvertebrate fauna of the Młynne stream (Polish Carpathians) in the aspect of the bed load transport and water quality. The qualitative composition of the bottom sediments and the bed load and suspended load transport along the mountain stream were presented. The studies were carried on the Młynne stream in Gorce (Polish Carpathians). The streams flows partially in the natural river-bed and partially in the regulated with rapids. The stream bed load is accumulated in the reservoir up to the check dam and is qualitatively different from the load deposited at the bars. The taxonomic richness of the Młynne stream is a little bigger compared with the other investigated mountain and sub-mountain streams, but lesser compared with bigger rivers. The number of taxa on the natural reaches is more than double than on the regulated ones, with more taxa of high environmental demands and high values of the BMWP-PL index. The paper brings up the question of the environmental friendly technical solutions in different human activities in the sub-mountain and mountain river valleys, advantageous for humans and the river biological diversity (or resistance for negative environmental factors) as well. Streszczenie Fauna makrobezkregowa na tle transportu rumowiska dennego oraz jakości wody w potoku Młynne w Karpatach Polskich. W artykule poruszono temat składu osadów dennych oraz transportu zawiesiny w potoku górskim. Badania przeprowadzono na potoku Młynne w Gorcach. Płynie on częściowo korytem naturalnym, a częściowo uregulowanym poprzez kaskady. Zawiesina denna jest akumulowana w zbiorniku powyżej zapory i pod względem jakościowym różni się od tej gromadzącej się na kaskadach. Fauna bezkręgowa jest dobrym wskaźnikiem zmian hydromorfologicznych i ich wpływu na ekosystem. Bogactwa gatunkowe potoku Młynne jest nieco większe niż innych badanych cieków górskich i podgórskich, ale mniejsze w porównaniu z większymi rzekami. Liczba taksonów na odcinkach naturalnych jest ponaddwukrotnie większa niż na uregulowanych, przy większym udziale gatunków bardzo dużych wymaganiach siedliskowych i dużych wartościach wskaźnika BMWP-PL. W pracy poruszono problem przyjaznych środowisku rozwiązań technicznych przy różnych rodzajach działalności ludzkiej w dolinach rzek górskich i podgórskich, mogących korzystnie wpływać zarówno na dobro człowieka, jak różnorodność biologiczną i odporność ekosystemu na niekorzystne czynniki zewnętrzne.
Deformation of Fuchsian representations and proper affine actions

The main goal of this article is to generalize Mess' work and using results from Labourie--Wentworth and Potrie--Sambarino, to show that inside Hitchin representations, infinitesimal deformations of Fuchsian representations of a cocompact surface group do not act properly along the directions corresponding to $k$-differentials. In the process, we introduce affine versions of cross ratios and triple ratios. We introduce Margulis invariants and relate them with affine cross ratios and infinitesimal Jordan projections. We obtain a general equivalent criterion for existence of proper affine actions in terms of the structure of the Margulis invariant spectra. Also, using a stability argument we show the existence of proper affine actions of non-abelian free groups whose linear part is a Hitchin representation.
Joint Signal Recovery and Graph Learning from Incomplete Time-Series

Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics, which might not be available in some scenarios. In this work, we aim to learn a graph from incomplete time-series observations. From another viewpoint, we consider the problem of semi-blind recovery of time-varying graph signals where the underlying graph model is unknown. We propose an algorithm based on the method of block successive upperbound minimization (BSUM), for simultaneous inference of the signal and the graph from incomplete data. Simulation results on synthetic and real time-series demonstrate the performance of the proposed method for graph learning and signal recovery.
Deciding the Feasibility and Minimizing the Height of Tangles

We study the following combinatorial problem. Given a set of $n$ y-monotone \emph{wires}, a \emph{tangle} determines the order of the wires on a number of horizontal \emph{layers} such that the orders of the wires on any two consecutive layers differ only in swaps of neighboring wires. Given a multiset~$L$ of \emph{swaps} (that is, unordered pairs of wires) and an initial order of the wires, a tangle \emph{realizes}~$L$ if each pair of wires changes its order exactly as many times as specified by~$L$. \textsc{List-Feasibility} is the problem of finding a tangle that realizes a given list~$L$ if such a tangle exists. \textsc{Tangle-Height Minimization} is the problem of finding a tangle that realizes a given list and additionally uses the minimum number of layers. \textsc{List-Feasibility} (and therefore \textsc{Tangle-Height Minimization}) is NP-hard [Yamanaka, Horiyama, Uno, Wasa; CCCG 2018]. We prove that \textsc{List-Feasibility} remains NP-hard if every pair of wires swaps only a constant number of times. On the positive side, we present an algorithm for \textsc{Tangle-Height Minimization} that computes an optimal tangle for $n$ wires and a given list~$L$ of swaps in $O((2|L|/n^2+1)^{n^2/2} \cdot \varphi^n \cdot n)$ time, where $\varphi \approx 1.618$ is the golden ratio and $|L|$ is the total number of swaps in~$L$. From this algorithm, we derive a simpler and faster version to solve \textsc{List-Feasibility}. We also use the algorithm to show that \textsc{List-Feasibility} is in NP and fixed-parameter tractable with respect to the number of wires. For \emph{simple} lists, where every swap occurs at most once, we show how to solve \textsc{Tangle-Height Minimization} in $O(n! \varphi^n)$ time.
Performance Comparison of Session-based Recommendation Algorithms based on GNNs

In session-based recommendation settings, a recommender system has to base its suggestions on the user interactions that are ob served in an ongoing session. Since such sessions can consist of only a small set of interactions, various approaches based on Graph Neural Networks (GNN) were recently proposed, as they allow us to integrate various types of side information about the items in a natural way. Unfortunately, a variety of evaluation settings are used in the literature, e.g., in terms of protocols, metrics and baselines, making it difficult to assess what represents the state of the art. In this work, we present the results of an evaluation of eight recent GNN-based approaches that were published in high-quality outlets. For a fair comparison, all models are systematically tuned and tested under identical conditions using three common datasets. We furthermore include k-nearest-neighbor and sequential rules-based models as baselines, as such models have previously exhibited competitive performance results for similar settings. To our surprise, the evaluation showed that the simple models outperform all recent GNN models in terms of the Mean Reciprocal Rank, which we used as an optimization criterion, and were only outperformed in three cases in terms of the Hit Rate. Additional analyses furthermore reveal that several other factors that are often not deeply discussed in papers, e.g., random seeds, can markedly impact the performance of GNN-based models. Our results therefore (a) point to continuing issues in the community in terms of research methodology and (b) indicate that there is ample room for improvement in session-based recommendation.
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks

Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification. We uncover that the predominant vulnerability is caused by the structural out-of-distribution (OOD) issue. This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs. We term such a methodology as LHS. To elaborate, our initial step involves learning a latent structure by employing a novel self-expressive technique based on multi-node interactions. Subsequently, the structure is refined using a pairwisely constrained dual-view contrastive learning approach. We iteratively perform the above procedure, enabling a GCN model to aggregate information in a homophilic way on heterophilic graphs. Armed with such an adaptable structure, we can properly mitigate the structural OOD threats over heterophilic graphs. Experiments on various benchmarks show the effectiveness of the proposed LHS approach for robust GCNs.
Free Variable as Effect, in Practice

Variable environment is the time-honored way of making sense of free variables, used in programming language theory as well when writing interpreters and some compilers. Algebraic effects give another way, as was pointed already at HOPE 2017. Although a theoretical curiosity, it may have surprising practical benefits: a new way of writing compilers, with the incremental type-checking, with easy variable usage, leaf function analyses. This work-in-progress report prototypes and illustrates the idea.
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity

Reinforcement Learning (RL) encompasses diverse paradigms, including model-based RL, policy-based RL, and value-based RL, each tailored to approximate the model, optimal policy, and optimal value function, respectively. This work investigates the potential hierarchy of representation complexity -- the complexity of functions to be represented -- among these RL paradigms. We first demonstrate that, for a broad class of Markov decision processes (MDPs), the model can be represented by constant-depth circuits with polynomial size or Multi-Layer Perceptrons (MLPs) with constant layers and polynomial hidden dimension. However, the representation of the optimal policy and optimal value proves to be $\mathsf{NP}$-complete and unattainable by constant-layer MLPs with polynomial size. This demonstrates a significant representation complexity gap between model-based RL and model-free RL, which includes policy-based RL and value-based RL. To further explore the representation complexity hierarchy between policy-based RL and value-based RL, we introduce another general class of MDPs where both the model and optimal policy can be represented by constant-depth circuits with polynomial size or constant-layer MLPs with polynomial size. In contrast, representing the optimal value is $\mathsf{P}$-complete and intractable via a constant-layer MLP with polynomial hidden dimension. This accentuates the intricate representation complexity associated with value-based RL compared to policy-based RL. In summary, we unveil a potential representation complexity hierarchy within RL -- representing the model emerges as the easiest task, followed by the optimal policy, while representing the optimal value function presents the most intricate challenge.
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey

Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pre-defined and fixed model interface that has limited interactivity and adaptability in following user' task instructions. To address them, Visual Instruction Tuning (VIT) has been intensively studied recently, which finetunes a large vision model with language as task instructions, aiming to learn from a wide range of vision tasks described by language instructions a general-purpose multimodal model that can follow arbitrary instructions and thus solve arbitrary tasks specified by the user. This work aims to provide a systematic review of visual instruction tuning, covering (1) the background that presents computer vision task paradigms and the development of VIT; (2) the foundations of VIT that introduce commonly used network architectures, visual instruction tuning frameworks and objectives, and evaluation setups and tasks; (3) the commonly used datasets in visual instruction tuning and evaluation; (4) the review of existing VIT methods that categorizes them with a taxonomy according to both the studied vision task and the method design and highlights the major contributions, strengths, and shortcomings of them; (5) the comparison and discussion of VIT methods over various instruction-following benchmarks; (6) several challenges, open directions and possible future works in visual instruction tuning research.
An efficient approach to characterize spatio-temporal dependence in cortical surface fMRI data

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique known for its ability to capture brain activity non-invasively and at fine spatial resolution (2-3mm). Cortical surface fMRI (cs-fMRI) is a recent development of fMRI that focuses on signals from tissues that have neuronal activities, as opposed to the whole brain. cs-fMRI data is plagued with non-stationary spatial correlations and long temporal dependence which, if inadequately accounted for, can hinder downstream statistical analyses. We propose a fully integrated approach that captures both spatial non-stationarity and varying ranges of temporal dependence across regions of interest. More specifically, we impose non-stationary spatial priors on the latent activation fields and model temporal dependence via fractional Gaussian errors of varying Hurst parameters, which can be studied through a wavelet transformation and its coefficients' variances at different scales. We demonstrate the performance of our proposed approach through simulations and an application to a visual working memory task cs-fMRI dataset.
Teacher learning in professional learning communities : a study of three primary schools in the Pinetown District of KwaZulu-Natal.

The purpose of this study was to explore teacher learning in Professional Learning Communities (PLCs) by investigating how teachers learn in a PLC. In addition it sought to explore the experiences of teachers’ professional learning in a PLC. Literature on PLCs has proliferated in the last decade, signifying the importance of collaborative learning practices. This study draws on Reid’s (2007) quadrants of teacher learning and Wenger’s (1998) community of practice theory to understand and interpret teacher learning in PLCs. This is a qualitative study, located within the interpretive paradigm. A case study research design was utilised with the case being three primary schools in the Pinetown district and it was the case of teacher learning in PLCs. Data generation methods included focus group interviews with six teachers in each school and semi-structured interviews with the principals of each school to triangulate the data. The findings of the research revealed that learning occurred predominantly in a collaborative capacity both formally and informally. Various modes of teacher learning were expressed: dialogue, workshops, networking and mentoring. However, dialogic practices were acknowledged as the most prevalent form of teacher learning in a PLC. It was further revealed that the nature of these dialogic practices were not thoroughly addressed in schools thereby hindering the reflective inquiry of engagement it requires for optimal teacher collaboration in a PLC. In terms of teachers’ experiences of learning in a PLC, occurrences were mostly positive. This was a result of engaging with social agents such as subject advisors and learning from each other. One of the most challenging factors of teacher learning in a PLC was the lack of support that was afforded to teachers. In addition the lack of time and work intensification hindered successful teacher learning in a PLC. These findings prompted the need for more involvement from leaders to support teacher learning in PLCs which was expressed as one of the recommendations. Furthermore, it was recommended that leaders play an integral role in establishing virtual PLCs for teachers to collaborate with members within and outside their organisation, allowing for networking and learning from external social agents. In this way the barrier of time as a hindering factor to teacher learning in a PLC would be addressed and teacher collaboration would be optimised.
Designing Fast Quantum Gates with Tunable Couplers: A Reinforcement Learning Approach

Fast quantum gates are crucial not only for the contemporary era of noisy intermediate-scale quantum devices but also for the prospective development of practical fault-tolerant quantum computing systems. Leakage errors, which arise from data qubits jumping beyond the confines of the computational subspace, are the main challenges in realizing non-adiabatically driven, fast gates. In this letter, we propose and illustrate the usefulness of reinforcement learning (RL) to generate fast two-qubit gates in practical multi-level superconducting qubits. In particular, we show that the RL controller offers great effectiveness in finding piecewise constant gate pulse sequences autonomously that act on two transmon data qubits coupled by a tunable coupler to generate a controlled-Z (CZ) gate with 11 ns gate time and an error rate of $\sim 4\times 10^{-3}$, making it about five times faster than state-of-the-art implementations. Such gate pulse sequences exploit the leakage space judiciously by controlling the leakage dynamics into and out of the computational subspace at appropriate times during the gate application, making it extremely fast.
Theory and Design of Space-Time Metallic Metasurfaces for Wireless Communications

This paper details a class of metal-based space-time metasurfaces for application in wireless communications scenarios. Concretely, we describe space-time metasurfaces that periodically alternate their properties in time between three spatial states:"air","conductor"and"grating". We analyze the physics of these metastructures via a computationally-efficient analytical technique based on the use of Floquet-Bloch series, integral equations and circuit models. By doing so, we reveal important features of these spatiotemporal metasurfaces: scattering parameters, field profiles, diffraction angles and nature of the space-time harmonics. The results, corroborated with a self-implemented numerical FDTD approach, show the potential application of these space-time metasurfaces as beamformers acting in reflection, in transmission or both. The amplitude and direction of the diffracted orders can be electronically controlled with the paramaters of the metasurface. Moreover, the intrinsic ability of time-modulated diffractive metasurfaces to mix and multiply frequencies is tested. We show how two different modulations can lead to the same diffraction angle but with different mixed output frequencies.
Vector Flows and the Capacity of a Discrete Memoryless Channel

One of the fundamental problems of information theory, since its foundation by Shannon in 1948, has been the computation of the capacity of a discrete memoryless channel, a quantity expressing the maximum rate at which information can travel through the channel. In the literature, several algorithms were proposed to approximately compute the capacity of a discrete memoryless channel, being an analytical solution unavailable for the general discrete memoryless channel. This paper presents a novel approach to compute the capacity, which is based on a continuous-time dynamical system. Such a dynamical system can indeed be regarded as a continuous-time version of the Blahut-Arimoto algorithm. In fact, the updating map appearing in the Blahut-Arimoto algorithm is here obtained as a suitable discretization of the vector flow presented, using an analogy with some game-theoretical models. Finally, this analogy suggests a high-level hardware circuit design enabling analog computation to estimate the capacity.
Interregional differences in MODIS‐derived cloud regimes

Cloud regimes based on histogram clustering offer a potentially useful tool for observational analysis of clouds. The utility of the regimes depends on their ability to identify cloud structures that are associated with distinct meteorological conditions. In this study, active remote sensing observations from CloudSat and CALIPSO are binned according to the cloud regimes derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The occurrence of CloudSat radar reflectivity by altitude, as well as CloudSat‐CALIPSO retrievals of cloud cover, precipitation occurrence, cloud radiative effect, and multilayered cloud structure are analyzed for each MODIS cloud regime and for different regions. While the picture of the regimes given by CloudSat and CALIPSO is generally consistent with that derived from MODIS, substantial region‐to‐region variability is found within the regimes. The regimes constrain the shortwave cloud radiative effect well, while the longwave effect and the precipitation occurrence exhibit more variability. The joint distributions of radar reflectivity and altitude also reveal differences in the structure of clouds within each regime. Thus, it appears that there is region‐dependent variability within each regime, resulting from the different meteorological environments. Among the major differences in the cloud structure are cloud top height in convective clouds and the number of distinct cloud layers in boundary layer clouds. Thus, passive optical sensors appear limited in their ability to characterize clouds and assign them to distinct regimes. The differences can be used to estimate the cloud regime inherent variability for studies that use them as a proxy for the climate effects of clouds.
InsActor: Instruction-driven Physics-based Characters

Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications. However, generating physically simulated animations that reflect high-level human instructions remains a difficult problem due to the complexity of physical environments and the richness of human language. In this paper, we present InsActor, a principled generative framework that leverages recent advancements in diffusion-based human motion models to produce instruction-driven animations of physics-based characters. Our framework empowers InsActor to capture complex relationships between high-level human instructions and character motions by employing diffusion policies for flexibly conditioned motion planning. To overcome invalid states and infeasible state transitions in planned motions, InsActor discovers low-level skills and maps plans to latent skill sequences in a compact latent space. Extensive experiments demonstrate that InsActor achieves state-of-the-art results on various tasks, including instruction-driven motion generation and instruction-driven waypoint heading. Notably, the ability of InsActor to generate physically simulated animations using high-level human instructions makes it a valuable tool, particularly in executing long-horizon tasks with a rich set of instructions.