Miracle-dz/UniFAR-FTUs huggingface dataset:
Moco: A Learnable Meta Optimizer for Combinatorial Optimization

Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from data. Many approaches utilize a neural network to directly construct a solution, but are limited in further improving based on already constructed solutions at inference time. Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state. This meta training procedure targets the overall best solution found during the search procedure given information such as the search budget. This allows Moco to adapt to varying circumstances such as different computational budgets. Moco is a fully learnable meta optimizer that does not utilize any problem specific local search or decomposition. We test Moco on the Traveling Salesman Problem (TSP) and Maximum Independent Set (MIS) and show that it outperforms other approaches on MIS and is overall competitive on the TSP, especially outperforming related approaches, partially even if they use additional local search.
Risk factors associated prevalence of hard tick in large ruminants of coastal areas of South Gujarat, India

A total of 2617 large animals (1682 cattle and 935 buffaloes) of coastal districts (Bharuch, Surat, Navsari and Valsad) of South Gujarat were screened for tick infestation. The tick’s prevalence rate was (p less than 0.05) significantly higher in cross-breed (44.81%) as compared to indigenous cattle (22.93%)/ buffaloes (32.26%). Prevalence of Rhipicephalus (Boophilus) microplus, Hyalomma spp. , Haemaphysalis spp. and mixed tick infestation was 57.80, 13.05, 20.32 and 8.83%, respectively. Infestation rate in large animals of less than 3 years had increased up to the level of 78.28%. Animals with poor body condition/ anaemia, kept in loose house (80.53%) and allowed group feeding (70.56%) had noted higher (p less than 0.05) prevalence rate of tick. Prevalence was highest in summer (41.84%) followed by rainy (40.65%) and winter (17.50%) season. It is quite demanding to know the area-wise prevalence of the hard ticks infecting animals.
Leveraging LLMs for Unsupervised Dense Retriever Ranking

This paper introduces a novel unsupervised technique that utilizes large language models (LLMs) to determine the most suitable dense retriever for a specific test(target) corpus. Selecting the appropriate dense retriever is vital for numerous IR applications that employ these retrievers, trained on public datasets, to encode or conduct searches within a new private target corpus. The effectiveness of a dense retriever can significantly diminish when applied to a target corpus that diverges in domain or task from the original training set. The problem becomes more pronounced in cases where the target corpus is unlabeled, e.g. in zero-shot scenarios, rendering direct evaluation of the model's effectiveness on the target corpus unattainable. Therefore, the unsupervised selection of an optimally pre-trained dense retriever, especially under conditions of domain shift, emerges as a critical challenge. Existing methodologies for ranking dense retrievers fall short in addressing these domain shift scenarios. To tackle this, our method capitalizes on LLMs to create pseudo-relevant queries, labels, and reference lists by analyzing a subset of documents from the target corpus. This allows for the ranking of dense retrievers based on their performance with these pseudo-relevant signals. Significantly, this strategy is the first to depend exclusively on the target corpus data, removing the necessity for training data and test labels. We assessed the effectiveness of our approach by compiling a comprehensive pool of cutting-edge dense retrievers and comparing our method against traditional dense retriever selection benchmarks. The findings reveal that our proposed solution surpasses the existing benchmarks in both the selection and ranking of dense retrievers.
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning

Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life. Adaptive online path planning in 3D environments is challenging due to the large set of valid actions and the presence of unknown occlusions. To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments. A key aspect of our approach is a dynamically constructed graph that restricts planning actions local to the robot, allowing us to quickly react to newly discovered obstacles and targets of interest. For replanning, we propose a new reward function that balances between exploring the unknown environment and exploiting online-collected data about the targets of interest. Our experiments show that our method enables more efficient target detection compared to state-of-the-art learning and non-learning baselines. We also show the applicability of our approach for orchard monitoring using an unmanned aerial vehicle in a photorealistic simulator.
Reconfigurable Intelligent Surface for Industrial Automation: mmWave Propagation Measurement, Simulation, and Control Algorithm Requirements

Reconfigurable intelligent surfaces (RISs) enable reliable low-latency millimeter wave (mmWave) communication links in cases of a blocked line-of-sight (LoS) between the base station (BS) and the user equipment (UE), i.e. a RIS mounted on a wall or the ceiling provides a bypass for the radio communication link. We present an active RIS with 127 patch antenna elements arranged in a hexagonal grid for a center frequency of 23.8 GHz. Each RIS element uses an orthogonal polarization transformation to enable amplification using a field-effect transistor (FET). The source and drain voltages of each FET is controlled using two bits. We assume that the coordinates of the UE in an industrial control scenario are known to the RIS. We measure the received power on a 2D grid of 60 cm by 100 cm with the RIS working in reflective and active mode. The results show that the RIS can successfully focus the radio signal at the desired target points. The half-power beam width is characterized in axial and radial directions with respect to the RIS position, obtaining a practical RIS configuration update criterion for a mobile UE. These results clearly show that RISs are prominent solutions for enabling reliable wireless communication in indoor industrial scenarios.
Multi-Scale Semantic Segmentation with Modified MBConv Blocks

Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks. Despite their success, their application in semantic segmentation has remained relatively unexplored. This paper introduces a novel adaptation of MBConv blocks specifically tailored for semantic segmentation. Our modification stems from the insight that semantic segmentation requires the extraction of more detailed spatial information than image classification. We argue that to effectively perform multi-scale semantic segmentation, each branch of a U-Net architecture, regardless of its resolution, should possess equivalent segmentation capabilities. By implementing these changes, our approach achieves impressive mean Intersection over Union (IoU) scores of 84.5% and 84.0% on the Cityscapes test and validation datasets, respectively, demonstrating the efficacy of our proposed modifications in enhancing semantic segmentation performance.
Spaces of metrics are Baire

For a metrizable space, we consider the space of all metrics generating the same topology on the metrizable space, and this space of metrics is equipped with the supremum metric. In this paper, for every metrizable space, we establish that the space of metrics on the metrizable space is Baire. We also show that the set of all complete metrics is comeager in the space of metrics.
Strong convexity-guided hyper-parameter optimization for flatter losses

We propose a novel white-box approach to hyper-parameter optimization. Motivated by recent work establishing a relationship between flat minima and generalization, we first establish a relationship between the strong convexity of the loss and its flatness. Based on this, we seek to find hyper-parameter configurations that improve flatness by minimizing the strong convexity of the loss. By using the structure of the underlying neural network, we derive closed-form equations to approximate the strong convexity parameter, and attempt to find hyper-parameters that minimize it in a randomized fashion. Through experiments on 14 classification datasets, we show that our method achieves strong performance at a fraction of the runtime.
Seamounts, deep-sea corals and fisheries. Vulnerability of deep-sea corals to fishing on seamounts beyond areas of national jurisdiction. UNEP-WCMC Biodiversity Series 25

Seamounts, deep-sea corals and fisheries - an ubiquitous ocean floor feature, a key marine ecosystem, and an important human activity: together they have created one of the most critical ocean issues. The report reveals the global scale of the vulnerability of habitat-forming stony corals on seamounts, and that of associated marine biodiversity and assemblages, to the impacts of trawling, especially in areas beyond national jurisdiction. It provides some of the best scientific evidence to date to support the call for concerted and urgent action on the high seas to protect seamount communities and their associated resources from the adverse effects of deep-water fishing. The report describes the results of data analyses that were used to understand the global distribution of deep-sea corals on seamounts, to model the distribution of suitable habitat for stony corals, and to appreciate the extent of the trawl fisheries on seamounts in areas beyond national jurisdiction. These results were combined, along with knowledge of the effects of trawling on corals and other seamount species, to identify the main areas at risk from the impact of current and future trawling on the high seas. In particular, seamount ecosystems in the Indian, North and South Atlantic, and the South Pacific Oceans, are threatened by the expansion of alfonsino (250-750 metres) and orange roughy (750-1200 metres) fisheries. ;
Statistical mechanics of elastica for the shape of supercoiled DNA: hyperelliptic elastica of genus three

This article studies the statistical mechanics of elastica as a model of the shapes of the supercoiled DNA, and shows that its excited states can be characterized by the focusing modified KdV (MKdV) equation due to thermal fluctuation. Following the previous paper (Matsutani and Previato, Physica D 430 (2022) 133073), the hyperelliptic solutions of the focusing modified KdV (MKdV) equation of genus three are considered. There appears a pattern as a repetition of the modulation of figure-eight and the inverse 'S' as a thermal fluctuation of elastica, called the S-eight mode. Our model states that the excited states of elastica due to the thermal effect have the S-eight mode, which reproduces the shapes of the AFM image of the supercoiled DNAs observed by Japaridze et al. (Nano Lett. 17 3, (2017) 1938).
Mesoscale fluxes over heterogeneous flat landscapes larger scale models for use in

We have developed a framework to parameterize mesoscale fluxes of heat, moisture, and momentum, in a cloud-free environment, which result from heterogeneous heating of flat land surfaces. The importance and parameterizability of these mesoscale fluxes is demonstrated using the mathematical concept of predictability. This methodology is used to estimate the relative importance of mesoscale, as contrasted with turbulent fluxes, in the Konza Prairie of Kansas during the FIFE field experiment. 0 1997 Elsevier Science B.V.
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Large Multi-View Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. Our key insights are two-fold: 1) 3D Representation: We propose multi-view Gaussian features as an efficient yet powerful representation, which can then be fused together for differentiable rendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models. Extensive experiments demonstrate the high fidelity and efficiency of our approach. Notably, we maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.
FairWire: Fair Graph Generation

Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for the deployment of them in real-world decision systems. In addition, while synthetic graph generation has become pivotal for privacy and scalability considerations, the impact of generative learning algorithms on the structural bias has not yet been investigated. Motivated by this, this work focuses on the analysis and mitigation of structural bias for both real and synthetic graphs. Specifically, we first theoretically analyze the sources of structural bias that result in disparity for the predictions of dyadic relations. To alleviate the identified bias factors, we design a novel fairness regularizer that offers a versatile use. Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model. Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs.
A topological algorithm for the Fourier transform of Stokes data at infinity

We reinterpret a result of T. Mochizuki about the Fourier transform of Stokes data of irregular connections on the Riemann sphere in the language of Stokes local systems due to P. Boalch. We thus obtain a clean topological description of the Stokes matrices of the Fourier transform from infinity to infinity in a large number of cases of one level. In particular, this induces explicit isomorphisms between wild character varieties, in a much larger range of examples than those for which such isomorphisms have previously been written down. We conjecture that these isomorphisms are compatible with the quasi-Hamiltonian structure of the wild character varieties.
Riemann-Lebesgue Forest for Regression

We propose a novel ensemble method called Riemann-Lebesgue Forest (RLF) for regression. The core idea of RLF is to mimic the way how a measurable function can be approximated by partitioning its range into a few intervals. With this idea in mind, we develop a new tree learner named Riemann-Lebesgue Tree which has a chance to split the node from response $Y$ or a direction in feature space $\mathbf{X}$ at each non-terminal node. We generalize the asymptotic performance of RLF under different parameter settings mainly through Hoeffding decomposition \cite{Vaart} and Stein's method \cite{Chen2010NormalAB}. When the underlying function $Y=f(\mathbf{X})$ follows an additive regression model, RLF is consistent with the argument from \cite{Scornet2014ConsistencyOR}. The competitive performance of RLF against original random forest \cite{Breiman2001RandomF} is demonstrated by experiments in simulation data and real world datasets.
Architectural Design Decisions for Self-Serve Data Platforms in Data Meshes

Data mesh is an emerging decentralized approach to managing and generating value from analytical enterprise data at scale. It shifts the ownership of the data to the business domains closest to the data, promotes sharing and managing data as autonomous products, and uses a federated and automated data governance model. The data mesh relies on a managed data platform that offers services to domain and governance teams to build, share, and manage data products efficiently. However, designing and implementing a self-serve data platform is challenging, and the platform engineers and architects must understand and choose the appropriate design options to ensure the platform will enhance the experience of domain and governance teams. For these reasons, this paper proposes a catalog of architectural design decisions and their corresponding decision options by systematically reviewing 43 industrial gray literature articles on self-serve data platforms in data mesh. Moreover, we used semi-structured interviews with six data engineering experts with data mesh experience to validate, refine, and extend the findings from the literature. Such a catalog of design decisions and options drawn from the state of practice shall aid practitioners in building data meshes while providing a baseline for further research on data mesh architectures.
Nash Equilibria in Reverse Temporal Voronoi Games

We study Voronoi games on temporal graphs as introduced by Boehmer et al. (IJCAI 2021) where two players each select a vertex in a temporal graph with the goal of reaching the other vertices earlier than the other player. In this work, we consider the reverse temporal Voronoi game, that is, a player wants to maximize the number of vertices reaching her earlier than the other player. Since temporal distances in temporal graphs are not symmetric in general, this yields a different game. We investigate the difference between the two games with respect to the existence of Nash equilibria in various temporal graph classes including temporal trees, cycles, grids, cliques and split graphs. Our extensive results show that the two games indeed behave quite differently depending on the considered temporal graph class.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

Graph transformers typically lack direct pair-to-pair communication, instead forcing neighboring pairs to exchange information via a common node. We propose the Triplet Graph Transformer (TGT) that enables direct communication between two neighboring pairs in a graph via novel triplet attention and aggregation mechanisms. TGT is applied to molecular property prediction by first predicting interatomic distances from 2D graphs and then using these distances for downstream tasks. A novel three-stage training procedure and stochastic inference further improve training efficiency and model performance. Our model achieves new state-of-the-art (SOTA) results on open challenge benchmarks PCQM4Mv2 and OC20 IS2RE. We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning. We also demonstrate the generality of TGT with SOTA results on the traveling salesman problem (TSP).
Making Multicurves Cross Minimally on Surfaces

On an orientable surface $S$, consider a collection $\Gamma$ of closed curves. The (geometric) intersection number $i_S(\Gamma)$ is the minimum number of self-intersections that a collection $\Gamma'$ can have, where $\Gamma'$ results from a continuous deformation (homotopy) of $\Gamma$. We provide algorithms that compute $i_S(\Gamma)$ and such a $\Gamma'$, assuming that $\Gamma$ is given by a collection of closed walks of length $n$ in a graph $M$ cellularly embedded on $S$, in $O(n \log n)$ time when $M$ and $S$ are fixed. The state of the art is a paper of Despr\'e and Lazarus [SoCG 2017, J. ACM 2019], who compute $i_S(\Gamma)$ in $O(n^2)$ time, and $\Gamma'$ in $O(n^4)$ time if $\Gamma$ is a single closed curve. Our result is more general since we can put an arbitrary number of closed curves in minimal position. Also, our algorithms are quasi-linear in $n$ instead of quadratic and quartic, and our proofs are simpler and shorter. We use techniques from two-dimensional topology and from the theory of hyperbolic surfaces. Most notably, we prove a new property of the reducing triangulations introduced by Colin de Verdi\`ere, Despr\'e, and Dubois [SODA 2024], reducing our problem to the case of surfaces with boundary. As a key subroutine, we rely on an algorithm of Fulek and T\'oth [JCO 2020].
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks

Recently, neural networks utilizing periodic activation functions have been proven to demonstrate superior performance in vision tasks compared to traditional ReLU-activated networks. However, there is still a limited understanding of the underlying reasons for this improved performance. In this paper, we aim to address this gap by providing a theoretical understanding of periodically activated networks through an analysis of their Neural Tangent Kernel (NTK). We derive bounds on the minimum eigenvalue of their NTK in the finite width setting, using a fairly general network architecture which requires only one wide layer that grows at least linearly with the number of data samples. Our findings indicate that periodically activated networks are \textit{notably more well-behaved}, from the NTK perspective, than ReLU activated networks. Additionally, we give an application to the memorization capacity of such networks and verify our theoretical predictions empirically. Our study offers a deeper understanding of the properties of periodically activated neural networks and their potential in the field of deep learning.
Embedding Knowledge Graphs in Degenerate Clifford Algebras

Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have been studied in the context of knowledge graph embeddings. We propose to consider nilpotent base vectors with a nilpotency index of two. In these spaces, denoted $Cl_{p,q,r}$, allows generalizing over approaches based on dual numbers (which cannot be modelled using $Cl_{p,q}$) and capturing patterns that emanate from the absence of higher-order interactions between real and complex parts of entity embeddings. We design two new models for the discovery of the parameters $p$, $q$, and $r$. The first model uses a greedy search to optimize $p$, $q$, and $r$. The second predicts $(p, q,r)$ based on an embedding of the input knowledge graph computed using neural networks. The results of our evaluation on seven benchmark datasets suggest that nilpotent vectors can help capture embeddings better. Our comparison against the state of the art suggests that our approach generalizes better than other approaches on all datasets w.r. t. the MRR it achieves on validation data. We also show that a greedy search suffices to discover values of $p$, $q$ and $r$ that are close to optimal.