Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Variable Fuzzy Set Theory Method Based on Entropy Weight to Evaluation Water Pollution

Water pollution evaluation is the important foundation on water pollution prevention and control,in view of the uncertainty of evaluation,variable fuzzy set theory is applied in the assessment of water pollution,first,established the reasonable index system and rating standard,then,took entropy weight into this method,used index experimental data volatility to calculate the index weight,at last,applied fuzzy variable evaluation model to evaluate water pollution,took will mean for the final evaluation results,the method is applied in taizhou nw tongyang canal water pollution evaluation,results show,compared with fuzzy comprehensive evaluation,variable fuzzy sets method based on entropy weight to evaluate the result is reasonable, objective,and has better reliability and stability,provides a new research methods and ideas for water pollution evaluation work.
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models

Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline1, which allows practitioners to estimate the energy needs of their models in advance, without actually running or training them. We accomplished this, by collecting high-quality energy data and building a first baseline model, capable of predicting the energy consumption of DL models by accumulating their estimated layer-wise energies.
Energy Consumption Evaluation of Optane DC Persistent Memory for Indexing Data Structures

The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using well-established indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.
Experimental study of a diaphragm discharge in water

In order to gain a better understanding of the discharge formation mechanism of a diaphragm discharge, the waveforms of the electrical current and the light emission from the plasma, as recorded with a photomultiplier, are compared. Time-averaged current-voltage characteristics are presented for discharges in solutions of NaCl in distilled water. The I/V-curves reveal that there are three different regimes of operation, depending on whether the diaphragm is filled, continuously or intermittently, with water, water vapour or plasma.
Volumetric Attribute Compression for 3D Point Clouds Using Feedforward Network with Geometric Attention

We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function f : ℝ3 → ℝ, we quantize and encode parameter vector θ that characterizes f at the encoder, for reconstruction ${f_{\hat \theta }}\left( {\mathbf{x}} \right)$ at known 3D points x’s at the decoder. Extending a previous work Region Adaptive Hierarchical Transform (RAHT) that employs piecewise constant functions to span a nested sequence of function spaces, we propose a feedforward linear network that implements higher-order B-spline bases spanning function spaces without eigen-decomposition. Feedforward network architecture means that the system is amenable to end-to-end neural learning. The key to our network is space-varying convolution, similar to a graph operator, whose weights are computed from the known 3D geometry for normalization. We show that the number of layers in the normalization at the encoder is equivalent to the number of terms in a matrix inverse Taylor series. Experimental results on real-world 3D point clouds show up to 2-3 dB gain over RAHT in energy compaction and 20-30% bitrate reduction.
On the Creativity of Large Language Models

Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article we firstly analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. In particular, we focus our discussion around the dimensions of value, novelty and surprise as proposed by Margaret Boden in her work. Then, we consider different classic perspectives, namely product, process, press and person. We discuss a set of ``easy'' and ``hard'' problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries, analyzing the opportunities offered by them, the challenges arising by them and the potential associated risks, from both legal and ethical points of view.
Measuring and Manipulating Knowledge Representations in Language Models

Neural language models (LMs) represent facts about the world described by text. Sometimes these facts derive from training data (in most LMs, a representation of the word banana encodes the fact that bananas are fruits). Sometimes facts derive from input text itself (a representation of the sentence I poured out the bottle encodes the fact that the bottle became empty). Tools for inspecting and modifying LM fact representations would be useful almost everywhere LMs are used: making it possible to update them when the world changes, to localize and remove sources of bias, and to identify errors in generated text. We describe REMEDI , an approach for querying and modifying factual knowledge in LMs. REMEDI learns a map from textual queries to fact encodings in an LM’s internal representation system. These encodings can be used as knowledge editors : by adding them to LM hidden representations, we can modify downstream generation to be consistent with new facts. REMEDI encodings can also be used as model probes : by comparing them to LM representations, we can ascertain what properties LMs attribute to mentioned entities, and predict when they will generate outputs that conﬂict with background knowledge or input text. REMEDI thus links work on probing, prompting, and model editing, and offers steps toward general tools for ﬁne-grained inspection and control of knowledge in LMs.
Convergent iLQR for Safe Trajectory Planning and Control of Legged Robots

In order to perform highly dynamic and agile maneuvers, legged robots typically spend time in underactuated domains (e.g. with feet off the ground) where the system has limited command of its acceleration and a constrained amount of time before transitioning to a new domain (e.g. foot touchdown). Meanwhile, these transitions can instantaneously change the system's state, possibly causing perturbations to be mapped arbitrarily far away from the target trajectory. These properties make it difficult for local feedback controllers to effectively recover from disturbances as the system evolves through underactuated domains and hybrid impact events. To address this, we utilize the fundamental solution matrix that characterizes the evolution of perturbations through a hybrid trajectory and its 2-norm, which represents the worst-case growth of perturbations. In this paper, the worst-case perturbation analysis is used to explicitly reason about the tracking performance of a hybrid trajectory and is incorporated in an iLQR framework to optimize a trajectory while taking into account the closed-loop convergence of the trajectory under an LQR tracking controller. The generated convergent trajectories recover more effectively from perturbations, are more robust to large disturbances, and use less feedback control effort than trajectories generated with traditional methods.
Knowledge Graph Embedding with 3D Compound Geometric Transformations

The cascade of 2D geometric transformations were exploited to model relations between entities in a knowledge graph (KG), leading to an effective KG embedding (KGE) model, CompoundE. Furthermore, the rotation in the 3D space was proposed as a new KGE model, Rotate3D, by leveraging its non-commutative property. Inspired by CompoundE and Rotate3D, we leverage 3D compound geometric transformations, including translation, rotation, scaling, reflection, and shear and propose a family of KGE models, named CompoundE3D, in this work. CompoundE3D allows multiple design variants to match rich underlying characteristics of a KG. Since each variant has its own advantages on a subset of relations, an ensemble of multiple variants can yield superior performance. The effectiveness and flexibility of CompoundE3D are experimentally verified on four popular link prediction datasets.
CV2X-LOCA: Roadside Unit-Enabled Cooperative Localization Framework for Autonomous Vehicles

An accurate and robust localization system is crucial for autonomous vehicles (AVs) to enable safe driving in urban scenes. While existing global navigation satellite system (GNSS)-based methods are effective at locating vehicles in open-sky regions, achieving high-accuracy positioning in urban canyons such as lower layers of multi-layer bridges, streets beside tall buildings, tunnels, etc., remains a challenge. In this paper, we investigate the potential of cellular-vehicle-to-everything (C-V2X) wireless communications in improving the localization performance of AVs under GNSS-denied environments. Specifically, we propose the first roadside unit (RSU)-enabled cooperative localization framework, namely CV2X-LOCA, that only uses C-V2X channel state information to achieve lane-level positioning accuracy. CV2X-LOCA consists of four key parts: data processing module, coarse positioning module, environment parameter correcting module, and vehicle trajectory filtering module. These modules jointly handle challenges present in dynamic C-V2X networks. Extensive simulation and field experiments show that CV2X-LOCA achieves state-of-the-art performance for vehicle localization even under noisy conditions with high-speed movement and sparse RSUs coverage environments. The study results also provide insights into future investment decisions for transportation agencies regarding deploying RSUs cost-effectively.
Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

The integrated shear 3-point correlation function ζ ± measures the correlation between the local shear 2-point function ξ ± and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, ζ ± can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using ζ ±. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on N-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to ξ ±-only, adding ζ ± can lead to ≈ 10-25% improvements on the constraints of parameters like As (or σ 8) and w 0. We find no evidence in ξ ± + ζ ± constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where ζ ± is measured, and of the strategy to estimate the covariance matrix (N-body vs. lognormal). Our analysis solidifies the strong potential of the ζ ± statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model

In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial. However, domain-specific conversational dialogue systems may be incoherent and introduce erroneous external information to answer questions due to the out-of-vocabulary issue or the wrong knowledge from the parameters of the neural network. In this work, we propose PK-Chat, a Pointer network guided Knowledge-driven generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Moreover, based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences. Finally, an academic dialogue benchmark is constructed to evaluate the quality of dialogue systems in academic scenarios and the source code is available online.
Crossword: A Semantic Approach to Data Compression via Masking

The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i.i. d. random variables or a stochastic process, thus establishing the fundamental limit as entropy for lossless compression and as mutual information for lossy compression. However, the source (including text, music, and speech) in the real world is often statistically ill-defined because of its close connection to human perception, and thus the model-driven approach can be quite suboptimal. This study places careful emphasis on English text and exploits its semantic aspect to enhance the compression efficiency further. The main idea stems from the puzzle crossword, observing that the hidden words can still be precisely reconstructed so long as some key letters are provided. The proposed masking-based strategy resembles the above game. In a nutshell, the encoder evaluates the semantic importance of each word according to the semantic loss and then masks the minor ones, while the decoder aims to recover the masked words from the semantic context by means of the Transformer. Our experiments show that the proposed semantic approach can achieve much higher compression efficiency than the traditional methods such as Huffman code and UTF-8 code, while preserving the meaning in the target text to a great extent.
A Framework and Call to Action for the Future Development of EMG-Based Input in HCI

Electromyography (EMG) has been explored as an HCI input modality following a long history of success for prosthesis control. While EMG has the potential to address a range of hands-free interaction needs, it has yet to be widely accepted outside of prosthetics due to a perceived lack of robustness and intuitiveness. To understand how EMG input systems can be better designed, we sampled the ACM digital library to identify limitations in the approaches taken. Leveraging these works in combination with our research group’s extensive interdisciplinary experience in this field, four themes emerged (1) interaction design, (2) model design, (3) system evaluation, and (4) reproducibility. Using these themes, we provide a step-by-step framework for designing EMG-based input systems to strengthen the foundation on which EMG-based interactions are built. Additionally, we provide a call-to-action for researchers to unlock the hidden potential of EMG as a widely applicable and highly usable input modality.
A model for compact x-ray sources: accretion by rotating magnetic stars

The physics of accretion onto compact stars is considered, taking into account both the effects of stellar rotation and a stellar magnetic field. We show that far from the star the stellar magnetic field is screened by currents flowing in the accreting plasma, while close to the star the stellar field forces matter to corotate with the star. The location of the Alfven surface, where the transition between the two regimes occurs, depends on the flow pattern of the accreting matter beyond the Alfvea surface and the rotation period of the compact star, as well as the mass accretion rate and the strength of the stellar magnetic field. Three types of flow pattern are considered: radial inflow toward the compact object, orbital motion about it, and streaming motion past it. For the accretion rates of interest the radius of the Alfven surface r/sub A/, is found to be ~10/sup 7/-10/sup 8/ cm for a typical neutron star and ~ 10/sup 8/ -10/sup 10/cm for a typical magnetic degenerate dwarf: For neutron stars it is shown that inside the Alfvea surface there is comparatively little flow of matter across field lines, so that accreting matter is channeled toward the magnetic polesmore » of the star where it forms hot spots. The resulting radiation is shown to emerge from the neighborhood of the stellar surface in a strongly anisotropic angular pattern with a spectrum which depends on the details of the accretion process; for an oblique rotator, one has a natural mechanism for the production of pulsed radiation, which is not generally expected to he blackbody in character, will be in excess of 6 keV. Comparison of the above model with the observations suggests strongly that the X-ray stars in the pulsating binary X-ray sources Cen X-3 and Her X-1 are accreting neutron stars with their magnetic axes inclined at substantial angles to their axes of rotation. The implications of this interpretation for the observed X-ray spectra and pulse wave-forms are discussed. The change in the X-ray pulsation period due to accretion of matter is calculated; the calculated time scales for the spin-up which occurs when there is orbital inflow toward a star rotating ia the same sense as the orbital motion, is in excellent agreement with these observed in Her X-1 and Gn X-3. The possibility of significant optical pulsations and the likely existence of a minimum pulsation period for accreting X-ray stars are discussed. (auth)« less
Rethinking Context Aggregation in Natural Image Matting

For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effectiveness of these modules has not been thoroughly explored. In this paper, we conduct extensive experiments to reveal that the context aggregation modules are actually not as effective as expected. We also demonstrate that when learned on large image patches, basic encoder-decoder networks with a larger receptive field can effectively aggregate context to achieve better performance.Upon the above findings, we propose a simple yet effective matting network, named AEMatter, which enlarges the receptive field by incorporating an appearance-enhanced axis-wise learning block into the encoder and adopting a hybrid-transformer decoder. Experimental results on four datasets demonstrate that our AEMatter significantly outperforms state-of-the-art matting methods (e.g., on the Adobe Composition-1K dataset, \textbf{25\%} and \textbf{40\%} reduction in terms of SAD and MSE, respectively, compared against MatteFormer). The code and model are available at \url{https://github.com/QLYoo/AEMatter}.
Extended SPR for Evolving Networks with Nodes Preferential Deletion

Evolving networks are more widely existed in real world than static networks, and studying their statistical characteristics is vital to recognize and explore them further. But for the networks with nodes preferential deletion, there are few researches due to the lack of effective methods. In this article, we propose an extended SPR (ESPR) for these preferential removal networks when discuss the essential statistics, especially the steady-state degree distribution. Comparing with continuum formalism that is often employed, this theory-supported method retains the actual topological structure and corresponding statistics of networks during evolving process. With two theorems, we demonstrate the effectiveness of ESPR in handling evolving networks with nodes non-uniform removal; moreover, it also be proved that the SPR is special case of ESPR. In other words, ESPR is an operative framework when deal with the degree distibution, and it even have potential to solve other statistics of evolving networks.
On the Impact of Regularization in Data-Driven Predictive Control

Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being controlled, which can be a time-consuming and expensive task. Data-driven predictive control (DDPC) methods offer an alternative approach that does not require an explicit mathematical model, but instead optimize the control policy directly from data. In this paper, we study the impact of two different regularization penalties on the closed-loop performance of a recently introduced data-driven method called $\gamma$ -DDPC. Moreover, we discuss the tuning of the related coefficients in different data and noise scenarios, to provide some guidelines for the end user.
Safe Perception-Based Control Under Stochastic Sensor Uncertainty Using Conformal Prediction

We consider perception-based control using state estimates that are obtained from high-dimensional sensor measurements via learning-enabled perception maps. However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior. Stochastic sensor noise can make matters worse and result in estimation errors that follow unknown distributions. We propose a perception-based control framework that i) quantifies estimation uncertainty of perception maps, and ii) integrates these uncertainty representations into the control design. To do so, we use conformal prediction to compute valid state estimation regions, which are sets that contain the unknown state with high probability. We then devise a sampled-data controller for continuous-time systems based on the notion of measurement robust control barrier functions. Our controller uses idea from self-triggered control and enables us to avoid using stochastic calculus. Our framework is agnostic to the choice of the perception map, independent of the noise distribution, and to the best of our knowledge the first to provide probabilistic safety guarantees in such a setting. We demonstrate the effectiveness of our proposed perception-based controller for a LiDAR-enabled F1/10th car.