Miracle-dz/UniFAR-FTUs huggingface dataset:
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation

Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments. Prior work has proposed building scene graphs of the environment but typically assumes that the environment is static, omitting regions that require active interactions. This severely limits their ability to handle more complex tasks in household and office environments: before setting up a table, robots must explore drawers and cabinets to locate all utensils and condiments. In this work, we introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment. The ACSG accounts for both low-level information, such as geometry and semantics, and high-level information, such as the action-conditioned relationships between different entities in the scene. To this end, we present the Robotic Exploration (RoboEXP) system, which incorporates the Large Multimodal Model (LMM) and an explicit memory design to enhance our system's capabilities. The robot reasons about what and how to explore an object, accumulating new information through the interaction process and incrementally constructing the ACSG. We apply our system across various real-world settings in a zero-shot manner, demonstrating its effectiveness in exploring and modeling environments it has never seen before. Leveraging the constructed ACSG, we illustrate the effectiveness and efficiency of our RoboEXP system in facilitating a wide range of real-world manipulation tasks involving rigid, articulated objects, nested objects like Matryoshka dolls, and deformable objects like cloth.
Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps

We develop nonparametric regression methods for the case when the true regression function is not necessarily smooth. More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true regression function lies in an $L_2$-fractional Sobolev space with order $s\in (0,1)$. This function class is a Hilbert space lying between the space of square-integrable functions and the first-order Sobolev space consisting of differentiable functions. It contains fractional power functions, piecewise constant or polynomial functions and bump function as canonical examples. For the proposed approach, we prove upper bounds on the in-sample mean-squared estimation error of order $n^{-\frac{2s}{2s+d}}$, where $d$ is the dimension, $s$ is the aforementioned order parameter and $n$ is the number of observations. We also provide preliminary empirical results validating the practical performance of the developed estimators.
EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems

Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly recognized for their ability to improve long-term user engagement. Yet, the field grapples with challenges such as the absence of accessible frameworks, inconsistent evaluation standards, and the complexity of replicating prior work. Addressing these obstacles, we present EasyRL4Rec, a user-friendly and efficient library tailored for RL-based RSs. EasyRL4Rec features lightweight, diverse RL environments built on five widely-used public datasets, and is equipped with comprehensive core modules that offer rich options to ease the development of models. It establishes consistent evaluation criteria with a focus on long-term impacts and introduces customized solutions for state modeling and action representation tailored to recommender systems. Additionally, we share valuable insights gained from extensive experiments with current methods. EasyRL4Rec aims to facilitate the model development and experimental process in the domain of RL-based RSs. The library is openly accessible at https://github.com/chongminggao/EasyRL4Rec.
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization

Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in steering Language Models (LMs) towards human values/goals. The key to the strategy is employing a reward model ({$\varphi$}) which can reflect a latent reward model with humans. While this strategy has proven to be effective, the training methodology requires a lot of human preference annotation (usually of the order of tens of thousands) to train {$\varphi$}. Such large-scale preference annotations can be achievable if the reward model can be ubiquitously used. However, human values/goals are subjective and depend on the nature of the task. This poses a challenge in collecting diverse preferences for downstream applications. To address this, we propose a novel methodology to infuse domain knowledge into {$\varphi$}, which reduces the size of preference annotation required. We validate our approach in E-Commerce Opinion Summarization, with a significant reduction in dataset size (just $940$ samples) while advancing the state-of-the-art. Our contributions include a novel Reward Modelling technique, a new dataset (PromptOpinSumm) for Opinion Summarization, and a human preference dataset (OpinPref). The proposed methodology opens avenues for efficient RLHF, making it more adaptable to diverse applications with varying human values. We release the artifacts for usage under MIT License.
Prediction of Fishbone Linear Instability in Tokamaks with Machine Learning Methods

A machine learning based surrogate model for fishbone linear instability in tokamaks is constructed. Hybrid simulations with the kinetic-magnetohydrodynamic (MHD) code M3D-K is used to generate the database of fishbone linear instability, through scanning the four key parameters which are thought to determine the fishbone physics. The four key parameters include (1) central total beta of both thermal plasma and fast ions, (2) the fast ion pressure fraction, (3) central value of safety factor $q$ and (4) the radius of $q=1$ surface. Four machine learning methods including linear regression, support vector machines (SVM) with linear kernel, SVM with nonlinear kernel and multi-layer perceptron are used to predict the fishbone instability, growth rate and real frequency, mode structure respectively. Among the four methods, SVM with nonlinear kernel performs very well to predict the linear instability with accuracy $\approx$95%, growth rate and real frequency with $R^2\approx$98%, mode structure with $R^2\approx$98%.
A Comparative Study of Parallel Reinforcement Learning Methods with a PC Cluster System

This paper presents a comparative study of three parallel implementation models for reinforcement learning. Two of them utilize Q-learning, and the other one utilizes fuzzy Q-learning for agent learning. In order to evaluate performance and validity of the three method, a PC (personal computer) cluster system consisting of 16 PCs connected via Gigabit ethernet has been built. For communications to deliver data among PCs, MPI (Message Passing Interface) is employed. Experimental results are compared with one another to show the performance and characteristics of the three methods.
DEEM: Dynamic Experienced Expert Modeling for Stance Detection

Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning method may neglect the domain knowledge to make a professional and accurate analysis. Thus, there is still room for improvement of LLMs reasoning, especially in leveraging the generation capability of LLMs to simulate specific experts (i.e., multi-agents) to detect the stance. In this paper, different from existing multi-agent works that require detailed descriptions and use fixed experts, we propose a Dynamic Experienced Expert Modeling (DEEM) method which can leverage the generated experienced experts and let LLMs reason in a semi-parametric way, making the experts more generalizable and reliable. Experimental results demonstrate that DEEM consistently achieves the best results on three standard benchmarks, outperforms methods with self-consistency reasoning, and reduces the bias of LLMs.
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies

Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially when it comes to modelling perceptual and subjective features. Distilling conceptual spaces from Large Language Models (LLMs) has recently emerged as a promising strategy. However, existing work has been limited to probing pre-trained LLMs using relatively simple zero-shot strategies. We focus in particular on the task of ranking entities according to a given conceptual space dimension. Unfortunately, we cannot directly fine-tune LLMs on this task, because ground truth rankings for conceptual space dimensions are rare. We therefore use more readily available features as training data and analyse whether the ranking capabilities of the resulting models transfer to perceptual and subjective features. We find that this is indeed the case, to some extent, but having perceptual and subjective features in the training data seems essential for achieving the best results. We furthermore find that pointwise ranking strategies are competitive against pairwise approaches, in defiance of common wisdom.
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation

The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the task becomes challenging. These shifts include differences in image appearance, intensity distribution, resolution, and aneurysm size, all of which complicate the segmentation process. To tackle these issues, we propose a novel domain generalization strategy that employs gradient surgery exponential moving average (GS-EMA) optimization technique coupled with boundary-aware contrastive learning (BACL). Our approach is distinct in its ability to adapt to new, unseen domains by learning domain-invariant features, thereby improving the robustness and accuracy of aneurysm segmentation across diverse clinical datasets. The results demonstrate that our proposed approach can extract more domain-invariant features, minimizing over-segmentation and capturing more complete aneurysm structures.
Performance Analysis of Systems with Coupled and Decoupled RISs

We analyze and compare different methods for handling the mutual coupling in RIS-aided communication systems. A new mutual coupling aware algorithm is derived where the reactance of each element is updated successively with a closed-form solution. In comparison to existing element-wise methods, this approach leads to a considerably reduced computational complexity. Furthermore, we introduce decoupling networks for the RIS array as a potential solution for handling mutual coupling. With these networks, the system model reduces to the same structure as when no mutual coupling were present. Including decoupling networks, we can optimize the channel gain of a RIS-aided SISO system in closed-form which allows to analyze the scenario under mutual coupling analytically and to draw connections to the conventional transmit array gain. In particular, a super-quadratic channel gain can be achieved which scales as N^4 where N is the number of RIS elements.
How to Sustain a Scientific Open-Source Software Ecosystem: Learning from the Astropy Project

Scientific open-source software (OSS) has greatly benefited research communities through its transparent and collaborative nature. Given its critical role in scientific research, ensuring the sustainability of such software has become vital. Earlier studies have proposed sustainability strategies for conventional scientific software and open-source communities. However, it remains unclear whether these solutions can be easily adapted to the integrated framework of scientific OSS and its larger ecosystem. This study examines the challenges and opportunities to enhance the sustainability of scientific OSS in the context of interdisciplinary collaboration, open-source community, and multi-project ecosystem. We conducted a case study on a widely-used software ecosystem in the astrophysics domain, the Astropy Project, using a mixed-methods design approach. This approach includes an interview with core contributors regarding their participation in an interdisciplinary team, a survey of disengaged contributors about their motivations for contribution, reasons for disengagement, and suggestions for sustaining the communities, and finally, an analysis of cross-referenced issues and pull requests to understand best practices for collaboration on the ecosystem level. Our study reveals the implications of major challenges for sustaining scientific OSS and proposes concrete suggestions for tackling these challenges.
Unlocking the Power of Large Language Models for Entity Alignment

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks.
Determining climate change impacts on ecosystems: the role of palaeontology

Climate change is projected to change the ecosystems on land and in the sea at rates that are unprecedented for millions of years. The most commonly used approach to derive projections of how ecosystems will look in the future are experiments on living organisms. By their nature, experiments are unlike the real world and cannot capture the ability of organisms to migrate, select and evolve. They are often limited to a select few species and drivers of environmental change and hence cannot represent the complexity of interactions in ‘real’ ecosystems. The fossil record is an archive of responses to climate change at a global ecosystem scale. If, and only if, fossil assemblage variation is combined with independent information of environmental changes, sensitives of species or higher taxa to a specific magnitude of change of an environmental driver can be determined and used to inform future vulnerabilities of this species to the same driver. While records are often fragmented, there are time intervals which, when thoroughly analysed with quantitative data, can provide valuable insights into the future of biodiversity on this planet. This review provides an overview of projected impacts on marine ecosystems including: (1) the range of neontological methods, observations and their challenges; and (2) the complementary information that palaeontologists can contribution to this global challenge. I advocate that, in collaborations with other disciplines, we should aim for a strong visibility of our field and the knowledge it can provide for policy relevant assessments of the future.
Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions

This technical report is an extended version of our OSDI 2020 paper: Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions. Abstract: When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we propose a preventive and adaptive failure mitigation service, NARYA, that is integrated in a production cloud, Microsoft Azure’s compute platform. Narya predicts imminent host failures based on multi-layer system signals and then decides smart mitigation actions. The goal is to avert VM failures. Narya’s decision engine takes a novel online experimentation approach to continually explore the best mitigation action. Narya further enhances the adaptive decision capability through reinforcement learning. Narya has been running in production for 15 months. It on average reduces VM interruptions by 26% compared to previous static strategy.
Speech Enhancement by Marginal Statistical Characterization in the Log Gabor Wavelet Domain

This work presents a fusion of Log Gabor Wavelet (LGW) and Maximum a Posteriori (MAP) estimator as a speech enhancement tool for acoustical background noise reduction. The probability density function (pdf) of the speech spectral amplitude is approximated by a Generalized Laplacian Distribution (GLD). Compared to earlier estimators the proposed method estimates the underlying statistical model more accurately by appropriately choosing the model parameters of GLD. Experimental results show that the proposed estimator yields a higher improvement in Segmental Signal-to-Noise Ratio (S-SNR) and lower Log-Spectral Distortion (LSD) in two different noisy environments compared to other estimators. Keywords—Speech Enhancement, Generalized Laplacian Distribution, Log Gabor Wavelet, Bayesian MAP Marginal Estimator.
MORFOGÊNESE DO DIVISOR HIDROGRÁFICO PARANÁ/PARAÍBA DO SUL: O CASO DA SUB-BACIA DO PARAÍBUNA

O presente artigo tem como objetivo analisar a morfogenese do relevo da borda oeste do Rift Continental do Sudeste do Brasil (RCSB) no divisor hidrografico Parana / Paraiba do Sul, localizado na Serra da Mantiqueira. Para tanto, serao realizadas analises morfologicas e morfometricas do relevo em bacias hidrograficas que drenam tanto para o interior do continente sobre o planalto no reverso da Serra da Mantiqueira – Bacia Hidrografica do Rio Grande - quanto para bacias que drenam a frente escarpada da serra em direcao ao Bacia Hidrografica do Rio Paraibuna. Utilizou-se como base para a realizacao do trabalho o levantamento de informacoes bibliograficas, analises em ambiente SIG, trabalhos de campo e elaboracao de mapas. A analise dos perfis topograficos e longitudinais dos rios indicam a existencia de morfologias de relevo planaltico para bacias que drenam a frente escarpada da Serra da Mantiqueira. A amplitude de relevo nas bacias que drenam o planalto interiorano e mais baixa que as bacias que drenam para o RCSB, variando entre 200m e 500m respectivamente. A densidade de knickpoints e maior nas bacias que drenam para o RCSB o que pode ser comprovado pela presenca de rupturas no perfil longitudinal dos rios. Os lineamentos estruturais apresentaram orientacao preferencial para N50W e N40W, este comportamento tambem pode ser observado entre as bacias interioranas e do graben do Paraiba do Sul quando analisadas separadamente. A orientacao dos canais de drenagem de 1ae 2a ordem indicou forte controle estrutural nos rios, uma vez que as orientacoes preferencias identificadas foram para o quadrante NW, principalmente para os canais associados a sub-bacia do rio Paraibuna que apresentou N50W como a direcao mais representativa. Em termos morfometricos, as taxas maximas e minimas de SPI apresentaram mudancas significativas nos valores entre as bacias, contudo, o valor medio das bacias se manteve semelhante. De maneira geral, as bacias que drenam para o RCSB apresentam os valores maximos mais elevados em relacao as bacias do planalto interiorano. Este comportamento aponta para um maior potencial erosivo nas bacias que drenam frentes escarpadas do que nas bacias situadas nos planaltos superiores com morfologia mais suave. A analise da rede de drenagem demonstrou feicoes geomorfologicas tipicas de anomalias, tais como cotovelos, e capturas fluviais principalmente, nas bacias afluentes da sub-bacia do rio Paraibuna. Estas feicoes indicam processos de reorganizacao fluvial por meio de capturas de drenagem, que ao capturar rio adjacentes, promovem mudanca na direcao do segmento do rio, formando um cotovelo, bem como, a formacao de um divisor rebaixado e um vale superdimensionado. O conjunto de analises aponta para o fato de que os afluentes da bacia hidrografica do Rio Paraibuna apresentam maior agressividade no processo de dissecacao do relevo do que aqueles que drenam as terras altas da Serra da Mantiqueira em direcao ao Rio Grande.
Effect of Channel Estimation on Capacity of MIMO System Employing Circular or Linear Receiving Array Antennas

Multiple Input Multiple Output (MIMO) wireless communication system employing a uniform linear array (ULA) at the transmitter and either a uniform linear array (ULA) or a uniform circular array (UCA) antenna at the receiver. The transmitter is assumed to be surrounded by scattering objects while the receiver is postulated to be free from scattering objects. The Laplacian distribution of angle of arrival (AOA) of a signal reaching the receiver is postulated. Calculations of the MIMO system capacity are performed for two cases without and with the channel estimation errors. For estimating the MIMO channel, the scaled least square (SLS) and minimum mean square error (MMSE) methods are considered.
From Satisfiability to Consistency through Certificates . Application to Partially Defined Constraints

Very often, it is easier to specify the set of solutions of a constraint than its filtering algorithm. In this paper, we propose a method to automatically derive a filtering algorithm from a constraint expressed by its satisfiability function. An interesting feature is that both satisfiability and consistency use the same data-structure. We give results on the level of consistency one can expect with respect to the representation. We give an application of the technique in the field of Machine Learning in which partially defined constraints are learned by decision trees.