Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generic Software Framework for Distributed Topological Analysis Pipelines

This system paper presents a software framework for the support of topological analysis pipelines in a distributed-memory model. While several recent papers introduced topology-based approaches for distributed-memory environments, these were reporting experiments obtained with tailored, mono-algorithm implementations. In contrast, we describe in this paper a general-purpose, generic framework for topological analysis pipelines, i.e. a sequence of topological algorithms interacting together, possibly on distinct numbers of processes. Specifically, we instantiated our framework with the MPI model, within the Topology ToolKit (TTK). While developing this framework, we faced several algorithmic and software engineering challenges, which we document in this paper. We provide a taxonomy for the distributed-memory topological algorithms supported by TTK, depending on their communication needs and provide examples of hybrid MPI+thread parallelizations. Detailed performance analyses show that parallel efficiencies range from $20\%$ to $80\%$ (depending on the algorithms), and that the MPI-specific preconditioning introduced by our framework induces a negligible computation time overhead. We illustrate the new distributed-memory capabilities of TTK with an example of advanced analysis pipeline, combining multiple algorithms, run on the largest publicly available dataset we have found (120 billion vertices) on a standard cluster with 64 nodes (for a total of 1,536 cores). Finally, we provide a roadmap for the completion of TTK's MPI extension, along with generic recommendations for each algorithm communication category.
Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images

Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at github.com/funkelab/cellulus.
Dimension-free Remez Inequalities and norm designs

The classical Remez inequality bounds the supremum of a bounded-degree polynomial on an interval $X$ by its supremum on any subset $Y\subset X$ of positive Lebesgue measure. There are many multivariate generalizations of the Remez inequality, but most have constants that depend strongly on dimension. Here we show that a broad class of domains $X$ and test sets $Y$ -- termed \emph{norm designs} -- enjoy dimension-free Remez-type estimates. Instantiations of this theorem allow us for example \emph{a}) to bound the supremum of an $n$-variate degree-$d$ polynomial on the solid cube $[0,1]^n$ by its supremum on the regular grid $\{0,1/d,2/d,\ldots, 1\}^n$ independent of dimension; and \emph{b}) in the case of a degree-$d$ polynomial $f:\mathbf{Z}_K^n\to\mathbf{C}$ on the $n$-fold product of cyclic groups of order $K$, to show the supremum of $f$ does not increase by more than $\mathcal{O}(\log K)^{2d}$ when $f$ is extended to the polytorus as $f:\mathbf{T}^n\to\mathbf{C}$.
Quintessence in the Weyl-Gauss-Bonnet model

Quintessence models have been widely examined in the context of scalar-Gauss-Bonnet gravity, a subclass of Horndeski's theory, and were proposed as viable candidates for Dark Energy. However, the relatively recent observational constraints on the speed of gravitational waves c GW have resulted in many of those models being ruled out because they predict c GW ≠ c generally. While these were formulated in the metric formalism of gravity, we put forward a new quintessence model with the scalar-Gauss-Bonnet action but in Weyl geometry, where the connection is not metric compatible. We find the fixed points of the dynamical system under some assumptions and determine their stability via linear analysis. The past evolution of the Universe can be reproduced correctly, but the late Universe constraints on c GW are grossly violated. Moreover, at these later stages tensor modes suffer from the gradient instabilities. We also consider the implications of imposing an additional constraint c GW = c, but this does not lead to evolution that is consistent with cosmological observations.
Discovering Fatigued Movements for Virtual Character Animation

Virtual character animation and movement synthesis have advanced rapidly during recent years, especially through a combination of extensive motion capture datasets and machine learning. A remaining challenge is interactively simulating characters that fatigue when performing extended motions, which is indispensable for the realism of generated animations. However, capturing such movements is problematic, as performing movements like backflips with fatigued variations up to exhaustion raises capture cost and risk of injury. Surprisingly, little research has been done on faithful fatigue modeling. To address this, we propose a deep reinforcement learning-based approach, which—for the first time in literature—generates control policies for full-body physically simulated agents aware of cumulative fatigue. For this, we first leverage Generative Adversarial Imitation Learning (GAIL) to learn an expert policy for the skill; Second, we learn a fatigue policy by limiting the generated constant torque bounds based on endurance time to non-linear, state- and time-dependent limits in the joint-actuation space using a Three-Compartment Controller (3CC) model. Our results demonstrate that agents can adapt to different fatigue and rest rates interactively, and discover realistic recovery strategies without the need for any captured data of fatigued movement.
Counterfactual Explanations for Time Series Forecasting

Among recent developments in time series forecasting methods, deep forecasting models have gained popularity as they can utilize hidden feature patterns in time series to improve forecasting performance. Nevertheless, the majority of current deep forecasting models are opaque, hence making it challenging to interpret the results. While counterfactual explanations have been extensively employed as a post-hoc approach for explaining classification models, their application to forecasting models still remains underexplored. In this paper, we formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF, that solves the problem by applying gradient-based perturbations to the original time series. The perturbations are further guided by imposing constraints to the forecasted values. We experimentally evaluate ForecastCF using four state-of-the-art deep model architectures and compare to two baselines. ForecastCF outperforms the baselines in terms of counterfactual validity and data manifold closeness, while generating meaningful and relevant counterfactuals for various forecasting tasks.
Effect of the brightest gamma-ray burst (GRB 221009A) on low energy gamma-ray counts at sea level

A gamma-ray burst, named GRB 221009A, occurred on 9 October 2022 and is the brightest ever observed GRB, whose frequency is now estimated as once in 10,000 years. This GRB was reported to be observed from many space missions, VLF receivers, and ground observations in optical and radio data. Additionally, a strikingly large number of very high energy (VHE) photons associated with this GRB were observed by the gamma-ray and cosmic ray observatory LHAASO. Though gamma rays of cosmic origin usually tend to be absorbed by the atmosphere, the high fluence of this GRB, along with the observation of more than 5000 VHE photons (0.5 to 18 TeV) by LHAASO from the ground, emphasises the need to explore other possible ground observations of this GRB. The present paper examines the effect of this GRB using gamma-ray data in a low energy range (0.2-6) MeV obtained using NaI (Tl) detectors located at Tirunelveli (Geographic coordinates: 8.71{\deg}N, 77.76{\deg}E), India. With RA = 288.3{\deg} and Dec = 19.8{\deg}, the exceptionally bright fluence of this GRB was geographically centred on India. We report no significant change in the observations associated with GRB 221009A. We discuss the extent of attenuation of gamma-rays in the atmosphere that could explain the reported observations. Further, we investigate the likelihood of ground observation of gamma-rays ($<$10 MeV) for a much more intense hypothetical GRB, and estimate the parameters, such as distance, fluence, and isotropic energy of such a GRB.
Consistent123: Improve Consistency for One Image to 3D Object Synthesis

Large image diffusion models enable novel view synthesis with high quality and excellent zero-shot capability. However, such models based on image-to-image translation have no guarantee of view consistency, limiting the performance for downstream tasks like 3D reconstruction and image-to-3D generation. To empower consistency, we propose Consistent123 to synthesize novel views simultaneously by incorporating additional cross-view attention layers and the shared self-attention mechanism. The proposed attention mechanism improves the interaction across all synthesized views, as well as the alignment between the condition view and novel views. In the sampling stage, such architecture supports simultaneously generating an arbitrary number of views while training at a fixed length. We also introduce a progressive classifier-free guidance strategy to achieve the trade-off between texture and geometry for synthesized object views. Qualitative and quantitative experiments show that Consistent123 outperforms baselines in view consistency by a large margin. Furthermore, we demonstrate a significant improvement of Consistent123 on varying downstream tasks, showing its great potential in the 3D generation field. The project page is available at consistent-123.github. io.
Uncertainty-Aware Planning for Heterogeneous Robot Teams using Dynamic Topological Graphs and Mixed-Integer Programming

Planning under uncertainty is a fundamental challenge in robotics. For multi-robot teams, the challenge is further exacerbated, since the planning problem can quickly become computationally intractable as the number of robots increase. In this paper, we propose a novel approach for planning under uncertainty using heterogeneous multi-robot teams. In particular, we leverage the notion of a dynamic topological graph and mixed-integer programming to generate multi-robot plans that deploy fast scout team members to reduce uncertainty about the environment. We test our approach in a number of representative scenarios where the robot team must move through an environment while minimizing detection in the presence of uncertain observer positions. We demonstrate that our approach is sufficiently computationally tractable for real-time re-planning in changing environments, can improve performance in the presence of imperfect information, and can be adjusted to accommodate different risk profiles.
AVO investigations of shallow marine sediments

Amplitude‐variation‐with‐offset (AVO) analysis is based on the Zoeppritz equations, which enable the computation of reflection and transmission coefficients as a function of offset or angle of incidence. High‐frequency (up to 700 Hz) AVO studies, presented here, have been used to determine the physical properties of sediments in a shallow marine environment (20 m water depth). The properties that can be constrained are P‐ and S‐wave velocities, bulk density and acoustic attenuation. The use of higher frequencies requires special analysis including careful geometry and source and receiver directivity corrections. In the past, marine sediments have been modelled as elastic materials. However, viscoelastic models which include absorption are more realistic. At angles of incidence greater than 40°, AVO functions derived from viscoelastic models differ from those with purely elastic properties in the absence of a critical angle of incidence. The influence of S‐wave velocity on the reflection coefficient is small (especially for low S‐wave velocities encountered at the sea‐floor). Thus, it is difficult to extract the S‐wave parameter from AVO trends. On the other hand, P‐wave velocity and density show a considerably stronger effect. Attenuation (described by the quality factor Q) influences the reflection coefficient but could not be determined uniquely from the AVO functions. In order to measure the reflection coefficient in a seismogram, the amplitudes of the direct wave and the sea‐floor reflection in a common‐midpoint (CMP) gather are determined and corrected for spherical divergence as well as source and streamer directivity. At CMP locations showing the different AVO characteristics of a mud and a boulder clay, the sediment physical properties are determined by using a sequential‐quadratic‐programming (SQP) inversion technique. The inverted sediment physical properties for the mud are: P‐wave velocity α=1450±25 m/s, S‐wave velocity β=90±35 m/s, density ρ=1220±45 kg/m3, quality factor for P‐wave QP=15±200, quality factor for S‐wave QS=10±30. The inverted sediment physical properties for the boulder clay are: α=1620±45 m/s,β=360±200 m/s,ρ=1380±85 kg/m3,QP=790±660,QS=25±10.
Spheres and fibres in turbulent flows at various Reynolds numbers

We perform fully coupled numerical simulations using immersed boundary methods of finite-size spheres and fibres suspended in a turbulent flow for a range of Taylor Reynolds numbers $12.8<Re_\lambda<442$ and solid mass fractions $0\le M\le1$. Both spheres and fibres reduce the turbulence intensity with respect to the single-phase flow at all Reynolds numbers, with fibres causing a more significant reduction than the spheres. Investigating the anomalous dissipation reveals that the particle effect tends to vanish as $Re\to\infty$. A scale-by-scale analysis shows that both particle shapes provide a `spectral shortcut' to the flow, but the shortcut extends further into the dissipative range in the case of fibres. Multifractal spectra of the near-particle dissipation show that spheres enhance dissipation in two-dimensional sheets, and fibres enhance the dissipation in structures with a dimension greater than one and less than two. In addition, we show that spheres suppress vortical flow structures, whereas fibres produce structures which completely overcome the turbulent vortex stretching behaviour in their vicinity.
Multi Level Dense Layer Neural Network Model for Housing Price Prediction

Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN
European Spatial Planning Systems, Social Models and Learning

Abstract An underlying aim of European territorial cooperation initiatives, such as INTERREG, is that they will lead to mutual understanding and learning across national boundaries. However, the effect of mutual learning on national systems and policies of planning is uncertain. After all, spatial planning systems are deeply embedded in their socio-economic, political and cultural context, which can potentially constrain the scope for mutual learning. Moreover, planning systems may have a certain degree of path-dependency, such as the persistence of institutions and cultures. In this paper, we explore the relationship between planning systems and their context and assess the extent to which cooperation and learning might contribute to convergence in styles of planning in Europe, as well as why and how this might be taking place. We take as our context the prevailing social model or model of society, the collection of common social and cultural values. We examine and compare typologies of planning systems and typologies of social models and find a degree of correspondence between them. The examples of England and the Netherlands are used to illustrate this interdependence. However, they also reveal how planning systems and policies in different contexts are changing in similar ways, and perhaps even demonstrate a measure of convergence. In other words, external factors may be overriding or undermining the influence of the national social model. The implementation of planning reforms may be running ahead of wider changes in the social model.
Fast Ray-Tracing-Based Precise Underwater Acoustic Localization without Prior Acknowledgment of Target Depth

Underwater localization is of great importance for marine observation and building positioning, navigation, timing (PNT) systems that could be widely applied in disaster warning, underwater rescues and resources exploration. The uneven distribution of underwater sound velocity poses great challenge for precise underwater positioning. The current soundline correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack of depth information, soundline tracking strategies cannot work well due to nonunique positional solutions. To tackle this issue, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of glancing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, so that fast ray tracing can be achieved. Then, we propose an sound velocity profile (SVP) simplification method, which reduces the computational cost of ray tracing. Experimental results show that the IRTUL has the most significant distance correction in the depth direction, and the average accuracy of IRTUL has been improved by about 3 meters compared to localization model with constant sound velocity. Also, the simplified SVP can significantly improve real-time performance with average accuracy loss less than 0.2 m when used for positioning.
Language Models are Universal Embedders

In the large language model (LLM) revolution, embedding is a key component of various systems. For example, it is used to retrieve knowledge or memories for LLMs, to build content moderation filters, etc. As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario. In this work, we make an initial step towards this goal, demonstrating that multiple languages (both natural and programming) pre-trained transformer decoders can embed universally when finetuned on limited English data. We provide a comprehensive practice with thorough evaluations. On English MTEB, our models achieve competitive performance on different embedding tasks by minimal training data. On other benchmarks, such as multilingual classification and code search, our models (without any supervision) perform comparably to, or even surpass heavily supervised baselines and/or APIs. These results provide evidence of a promising path towards building powerful unified embedders that can be applied across tasks and languages.
Enhancing Sample Quality through Minimum Energy Importance Weights

Importance sampling is a powerful tool for correcting the distributional mismatch in many statistical and machine learning problems, but in practice its performance is limited by the usage of simple proposals whose importance weights can be computed analytically. To address this limitation, Liu and Lee (2017) proposed a Black-Box Importance Sampling (BBIS) algorithm that computes the importance weights for arbitrary simulated samples by minimizing the kernelized Stein discrepancy. However, this requires knowing the score function of the target distribution, which is not easy to compute for many Bayesian problems. Hence, in this paper we propose another novel BBIS algorithm using minimum energy design, BBIS-MED, that requires only the unnormalized density function, which can be utilized as a post-processing step to improve the quality of Markov Chain Monte Carlo samples. We demonstrate the effectiveness and wide applicability of our proposed BBIS-MED algorithm on extensive simulations and a real-world Bayesian model calibration problem where the score function cannot be derived analytically.
Current approaches to seasonal to interannual climate predictions

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described. Copyright © 2001 Royal Meteorological Society
Bicomplex generalized hypergeometric functions and their applications

In this work, generalized hypergeometric functions for bicomplex argument is introduced and its convergence criteria is derived. Furthermore, integral representation of this function has been established. Moreover, quadratic transformation, differential relation, analyticity and contiguous relations of this function are derived. Additionally, applications in quantum information system and quantum optics are provided as a consequence.