Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Multi-Robot System for Non-myopic Spatial Sampling

This paper presents a distributed scalable multi-robot planning algorithm for non-uniform sampling of quasi-static spatial ﬁelds. We address the problem of eﬃcient data collection using multiple autonomous vehicles and consider the eﬀects of communication be-tween multiple robots, acting independently, on the over-all sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a ﬁxed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team conﬁgurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and ﬁeld deployment data. The results show that our sampling algorithm is eﬃ-cient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability our method in sampling large-scale environments.
Data-driven Weight Initialization with Sylvester Solvers

In this work, we propose a data-driven scheme to initialize the parameters of a deep neural network. This is in contrast to traditional approaches which randomly initialize parameters by sampling from transformed standard distributions. Such methods do not use the training data to produce a more informed initialization. Our method uses a sequential layer-wise approach where each layer is initialized using its input activations. The initialization is cast as an optimization problem where we minimize a combination of encoding and decoding losses of the input activations, which is further constrained by a user-defined latent code. The optimization problem is then restructured into the well-known Sylvester equation, which has fast and efficient gradient-free solutions. Our data-driven method achieves a boost in performance compared to random initialization methods, both before start of training and after training is over. We show that our proposed method is especially effective in few-shot and fine-tuning settings. We conclude this paper with analyses on time complexity and the effect of different latent codes on the recognition performance.
Efficient Temporal Piecewise-Linear Numeric Planning With Lazy Consistency Checking

Temporal planning often involves numeric effects that are directly proportional to their action’s duration. These include continuous effects, where a numeric variable is subjected to a rate of change while the action is being executed, and discrete duration-dependent effects, where the variable is updated instantaneously but the magnitude of such change is computed from the action’s duration. When these effects are linear, state-of-the-art temporal planners often make use of linear programming to ensure that these numeric updates are consistent with the chosen start times and durations of the plan’s actions. This is typically done for each evaluated state as a part of the search process. This exhaustive approach is not scalable to solve real-world problems that require long plans, because the size of the linear program becomes larger and slower to solve. In this article, we propose techniques that minimize this overhead by computing these checks more selectively and formulating linear programs that have a smaller footprint. The effectiveness of these techniques is demonstrated on domains that use a mix of discrete and continuous effects, which is typical of real-world planning problems. The resultant planner also outperforms most state-of-the-art temporal–numeric and hybrid planners, in terms of both coverage and scalability.
XGBoost energy consumption prediction based on multi-system data HVAC

The energy consumption of the HVAC system accounts for a significant portion of the energy consumption of the public building system, and using an efficient energy consumption prediction model can assist it in carrying out effective energy-saving transformation. Unlike the traditional energy consumption prediction model, this paper extracts features from large data sets using XGBoost, trains them separately to obtain multiple models, then fuses them with LightGBM's independent prediction results using MAE, infers energy consumption related variables, and successfully applies this model to the self-developed Internet of Things platform.
Lecture notes on non-convex algorithms for low-rank matrix recovery

Low-rank matrix recovery problems are inverse problems which naturally arise in various fields like signal processing, imaging and machine learning. They are non-convex and NP-hard in full generality. It is therefore a delicate problem to design efficient recovery algorithms and to provide rigorous theoretical insights on the behavior of these algorithms. The goal of these notes is to review recent progress in this direction for the class of so-called “non-convex algorithms”, with a particular focus on the proof techniques. Although they aim at presenting very recent research works, these notes have been written with the intent to be, as much as possible, accessible to non-specialists. These notes were written for an eight-hour lecture at Collège de France. The original version, in French, is available online and the videos of the lecture can be found on the Collège de France website. The beginning takes inspiration from the review articles [Davenport and Romberg, 2016] and [Chen and Chi, 2018].
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness

Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alleviating the resulting exploration problem. They usually incentivize agents to look for new states through novelty signals. Yet, such methods encourage exhaustive exploration of the state space rather than focusing on the environment's salient interaction opportunities. We propose a new exploration method, called Don't Do What Doesn't Matter (DoWhaM), shifting the emphasis from state novelty to state with relevant actions. While most actions consistently change the state when used, e.g. moving the agent, some actions are only effective in specific states, e.g., opening a door, grabbing an object. DoWhaM detects and rewards actions that seldom affect the environment. We evaluate DoWhaM on the procedurally-generated environment MiniGrid against state-of-the-art methods. Experiments consistently show that DoWhaM greatly reduces sample complexity, installing the new state-of-the-art in MiniGrid.
Investigation of the Electrostatic Atomization Method for Remote Injection and High Pressure

Extensive studies were undertaken in order to better understand the operation of electrostatic atomizers used in conjunction with dielectric liquids. Electrical and spray characterization studies were conducted for steady-voltage charge injection atomizers. The experimental work was complemented by further investigations concentrating on the analysis of spray breakup mechanisms and droplet size measurements. A further stage was undertaken by considering electrostatic charge injection method to be a part of a different type of charge injection atomizer. In this system, the charge injection process was separated from the liquid atomization process. With this design, the charge injection system could be retrofitted to a range of existing atomization systems. This makes the electrostatic atomization method more universal. Through the initial experiments with a commercially available nozzle, it was demonstrated that the charged liquid was successfully carried to a different location and sprayed under certain conditions.Imaging studies proved the improvement in finer drop size and wider spray plume coverage. Electrostatic charge injection method was applied at high hydrodynamic pressures, up to 40 bar, to evaluate the electrical and atomization performance at elevated Reynolds number (Re). The main focus was to investigate the effect of electrostatic charging on higher pressure injection systems in order to form finer sprays. Laser Diffraction Spectrometry (LDS) measurements and imaging studies were performed to investigate the effect of the electrostatic charge injection technique on drop size distribution. It was observed that spray dispersion was enhanced and drop size could be reduced with the increase in specific charge.
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units

We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques. Unlike standard structures where a single prediction is made, we adopt encoder-decoder models with sequence-to-sequence and Attention mechanisms to generate a forecasting path. Our methods achieve comparable performance to state-of-art algorithms at short prediction horizons. Importantly, they outperform when generating predictions over long horizons by leveraging the multi-horizon setup. Given that encoder-decoder models rely on recurrent neural layers, they generally suffer from slow training processes. To remedy this, we experiment with utilising novel hardware, so-called Intelligent Processing Units (IPUs) produced by Graphcore. IPUs are specifically designed for machine intelligence workload with the aim to speed up the computation process. We show that in our setup this leads to significantly faster training times when compared to training models with GPUs.
Multi-Color Balance For Color Constancy

In this paper, we propose a novel multi-color balance adjustment for color constancy. The proposed method, called n-color balancing allows us not only to perfectly correct n target colors on the basis of corresponding ground truth colors but also to correct colors other than the n colors. In contrast, although white-balancing can perfectly adjust white, colors other than white are not considered in the framework of white-balancing in general. In an experiment, the proposed multi-color balancing is demonstrated to outperform both conventional white and multi-color balance adjustments including Bradford’s model.
Learning to make consumption-saving decisions in a changing environment: an AI approach ∗

This exercise oﬀers an innovative learning mechanism to model economic agent’s decision-making process using a deep reinforcement learning algorithm. In particular, this AI agent has limited or no information on the underlying economic structure and its own preference. I model how the AI agent learns in terms of how it collects and processes information. It is able to learn in real time through constantly interacting with the environment and adjusting its actions accordingly. I illustrate that the economic agent under deep reinforcement learning is adaptive to changes in a given environment in real time. AI agents diﬀer in their ways of collecting and processing information, and this leads to diﬀerent learning behaviours and welfare distinctions. The chosen economic structure can be generalised to other decision-making processes and economic models.
Semantic Representation for Dialogue Modeling

Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.
Mechanical properties of 3D-printed pentadiamond

In this work, we combined fully atomistic molecular dynamics and finite elements simulations with mechanical testings to investigate the mechanical behavior of atomic and 3D-printed models of pentadiamond. Pentadiamond is a recently proposed new carbon allotrope, which is composed of a covalent network of pentagonal rings. Our results showed that the stress–strain (SS) behavior is almost scale-independent. The SS curves of the 3D-printed structures exhibit three characteristic regions. For low-strain values, this first region presents a non-linear behavior close to zero, followed by a well-defined linear behavior. The second regime is a quasi-plastic one and the third one is densification followed by structural failures (fracture). Young’s modulus values decrease from 520 to 486 MPa. The deformation mechanism is bending-dominated and different from the layer-by-layer deformation mechanism observed for other 3D-printed structures. They exhibit good energy absorption capabilities (3.5 MJ kg−1), with some structures even outperforming epoxy Kevlar and TRIP-steel. The structures show better absorption potential than the well-known porous architectures such as honeycomb, schwarzites, and tubulanes and occupy the same region of woven structures in the Ashby chart.
Band-gap engineering, magnetic behavior and Dirac-semimetal character in the MoSi2N4 nanoribbon with armchair and zigzag edges

Motivated by the recent successful formation of the MoSi2N4 monolayer (Hong et al 2020 Science 369, 670), the structural, electronic, and magnetic properties of MoSi2N4 nanoribbons (NRs) is investigated for the first time. The band structure calculations showed spin-polarization in zigzag edges and a non-magnetic semiconducting character in armchair edges. For armchair-edges, we identify an indirect to direct bandgap shift compared to the MoSi2N4 monolayer, and its energy gap increases with increasing NR width. Anisotropic electrical and magnetic behaviors are observed via band structure calculations at the zigzag and armchair edges, where, surprisingly, for one type of zigzag-edge configuration, we identifed a Dirac-semimetal character. The appearance of magnetism and Dirac-semimetal in MoSi2N4 ribbons can give rise to novel physical properties, which could be helpful in applications for next-generation electronic devices.
Fast-Racing: An Open-Source Strong Baseline for $\mathrm{SE}(3)$ Planning in Autonomous Drone Racing

With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula>, whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this letter proposes an open-source baseline, which includes a high-performance <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planner and a challenging simulation platform tailored for drone racing. We specify the <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> trajectory generation as a soft-penalty optimization problem, and speed up the solving process utilizing its underlying parallel structure. Moreover, to provide a testbed for challenging the planner, we develop delicate drone racing tracks which mimic real-world set-up and necessities planning in <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula>. Besides, we provide necessary system components such as common map interfaces and a baseline controller, to make our work plug-in-and-use. With our baseline, we hope to future foster the research of <inline-formula><tex-math notation="LaTeX">$\mathrm{SE}(3)$</tex-math></inline-formula> planning and the competition of autonomous drone racing.
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection

Intrusion detection is an essential task in the cyber threat environment. Machine learning and deep learning techniques have been applied for intrusion detection. However, most of the existing research focuses on the model work but ignores the fact that poor data quality has a direct impact on the performance of a machine learning system. More attention should be paid to the data work when building a machine learning-based intrusion detection system. This article first summarizes existing machine learning-based intrusion detection systems and the datasets used for building these systems. Then the data preparation workflow and quality requirements for intrusion detection are discussed. To figure out how data and models affect machine learning performance, we conducted experiments on 11 HIDS datasets using seven machine learning models and three deep learning models. The experimental results show that BERT and GPT were the best algorithms for HIDS on all of the datasets. However, the performance on different datasets varies, indicating the differences between the data quality of these datasets. We then evaluate the data quality of the 11 datasets based on quality dimensions proposed in this paper to determine the best characteristics that a HIDS dataset should possess in order to yield the best possible result. This research initiates a data quality perspective for researchers and practitioners to improve the performance of machine learning-based intrusion detection.
Absence of Space-Charge-Limited Current in Unconventional Field Emission

For field emission (FE), it is widely expected that its emitting current density $J$ will become space-charge-limited current (SCLC) due the built-up of charge in-transit within a gap spacing $D$ biased at sufficiently large voltage $V$. In this paper, we reveal a peculiar finding in which this expected two-stage transition (from FE to SCLC) is no longer valid for FE not obeying the traditional Fowler-Nordheim (FN) law. %Such effect arises when the non-FN based emitters fails to inject sufficient %charge current at high voltage to sustain the SCLC. By employing a generalized FN scaling of $\ln\left(J/V^k\right) \propto - 1/V$, we show the existence of a \emph{critical exponent} $k_c \equiv 3/2$ where unusual behaviours occur for $k<k_c$: (a) Only FE at small $D$ (no transition to SCLC even at infinitely large $V$), and (b) Three-stage transition from FE first to SCLC then back to FE at large $D$. For any $k>k_c$, the conventional two-stage transition from FE to SCLC will always occur for all $D$, which also includes the conventional FN law at $k$ = 2. Using various unconventional FE models with $k \neq 2$, we specifically demonstrate these peculiar transitions. Under a normalized model, our findings uncover the rich interplay between the source-limited FE and bulk-limited SCLC over a wide range of operating conditions.
Spatial-Temporal Conv-Sequence Learning With Accident Encoding for Traffic Flow Prediction

In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations. Current state-of-the-art methods for predicting traffic flow are based on graph architectures and sequence learning models, but they do not fully exploit dynamic spatial-temporal information in the traffic system. Specifically, the temporal dependencies in the short-range are diluted by recurrent neural networks. Moreover, local spatial information is also ignored by existing sequence models, because their convolution operation uses global average pooling. Besides, accidents may occur during object transition, which will cause congestion in the real world and further decrease prediction accuracy. To overcome these challenges, we propose Spatial-Temporal Conv-sequence Learning (STCL), where a focused temporal block uses unidirectional convolution to capture short-term periodic temporal dependencies effectively, and a spatial-temporal fusion module is responsible for extracting dependencies of interactions and decreasing the feature dimensions. Moreover, as the accidents features have an impact on local traffic congestion, we employ position encoding to detect anomalies in complex traffic situations. We have conducted a large number of experiments on real-world tasks and verified the effectiveness of our proposed method.
The Featureless HST/WFC3 Transmission Spectrum of the Rocky Exoplanet GJ 1132b: No Evidence for a Cloud-free Primordial Atmosphere and Constraints on Starspot Contamination

Orbiting an M dwarf 12 pc away, the transiting exoplanet GJ 1132b is a prime target for transmission spectroscopy. With a mass of 1.7 M ⊕ and radius of 1.1 R ⊕, GJ 1132b’s bulk density indicates that this planet is rocky. Yet with an equilibrium temperature of 580 K, GJ 1132b may still retain some semblance of an atmosphere. Understanding whether this atmosphere exists and its composition will be vital for understanding how the atmospheres of terrestrial planets orbiting M dwarfs evolve. We observe five transits of GJ 1132b with the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST). We find a featureless transmission spectrum from 1.1 to 1.7 μm, ruling out cloud-free atmospheres with metallicities <300× solar with >4. 8σ confidence. We combine our WFC3 results with transit depths from TESS and archival broadband and spectroscopic observations to find a featureless spectrum across 0.7 to 4.5 μm. GJ 1132b therefore has a high mean molecular weight atmosphere, possesses a high-altitude aerosol layer, or has effectively no atmosphere. Higher-precision observations are required in order to differentiate between these possibilities. We explore the impact of hot and cold starspots on the observed transmission spectrum GJ 1132b, quantifying the amplitude of spot-induced transit depth features. Using a simple Poisson model, we estimate spot temperature contrasts, spot covering fractions, and spot sizes for GJ 1132. These limits, as well as the modeling framework, may be useful for future observations of GJ 1132b or other planets transiting similarly inactive M dwarfs.