Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Exploitation of Redundancies in Reconfigurable Multirobot Systems

While traditional robotic systems come with a monolithic system design, reconfigurable multirobot systems can share and shift physical resources in an on-demand fashion. Multirobot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this article details an organization model as basis for planning with reconfigurable multirobot systems. The model allows us to exploit redundancies when optimizing a multirobot system’s probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multirobot missions. We use a simulated multirobot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape. Our implementation of the organization model is open-source available (https://github.com/rock-knowledge-reasoning/knowledge-reasoning-moreorg).
Synthesis of Maximally Permissive Covert Attackers Against Unknown Supervisors by Using Observations

In this paper, we consider the problem of synthesis of maximally permissive covert damage-reachable attackers in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto maximal permissiveness, assuming the model of the supervisor is known, and the maximal permissiveness that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of maximally permissive covert damage-reachable attackers, given the model of the plant and the finite set of observations, to the synthesis of maximally permissive safe supervisors for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
Team PyKale (xy9) Submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition

This report describes the technical details of our submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition. The EPIC-Kitchens dataset is more difficult than other video domain adaptation datasets due to multi-tasks with more modalities. Firstly, to participate in the challenge, we employ a transformer to capture the spatial information from each modality. Secondly, we employ a temporal attention module to model temporal-wise inter-dependency. Thirdly, we employ the adversarial domain adaptation network to learn the general features between labeled source and unlabeled target domain. Finally, we incorporate multiple modalities to improve the performance by a three-stream network with late fusion. Our network achieves the comparable performance with the state-of-the-art baseline T$A^3$N and outperforms the baseline on top-1 accuracy for verb class and top-5 accuracies for all three tasks which are verb, noun and action. Under the team name xy9, our submission achieved 5th place in terms of top-1 accuracy for verb class and all top-5 accuracies.
Physical Conditions in the LMC’s Quiescent Molecular Ridge: Fitting Non-LTE Models to CO Emission

The Molecular Ridge in the LMC extends several kiloparsecs south from 30 Doradus, and it contains ∼30% of the molecular gas in the entire galaxy. However, the southern end of the Molecular Ridge is quiescent—it contains almost no massive star formation, which is a dramatic decrease from the very active massive-star-forming regions 30 Doradus, N159, and N160. We present new Atacama Large Millimeter/submillimeter Array and Atacama Pathfinder Experiment observations of the Molecular Ridge at a resolution as high as ∼16″ (∼3.9 pc) with molecular lines 12CO(1-0), 13CO(1-0), 12CO(2-1), 13CO(2-1), and CS(2-1). We analyze these emission lines with our new multiline non-LTE fitting tool to produce maps of T kin, nH2 , and N CO across the region based on models from RADEX. Using simulated data for a range of parameter space for each of these variables, we evaluate how well our fitting method can recover these physical parameters for the given set of molecular lines. We then compare the results of this fitting with LTE and X CO methods of obtaining mass estimates and how line ratios correspond with physical conditions. We find that this fitting tool allows us to more directly probe the physical conditions of the gas and estimate values of T kin, nH2 , and N CO that are less subject to the effects of optical depth and line-of-sight projection than previous methods. The fitted nH2 values show a strong correlation with the presence of young stellar objects (YSOs), and with the total and average mass of the associated YSOs. Typical star formation diagnostics, such as mean density, dense gas fraction, and virial parameter do not show a strong correlation with YSO properties.
Listen to Your Favorite Melodies with img2Mxml, Producing MusicXML from Sheet Music Image by Measure-based Multimodal Deep Learning-driven Assembly

Deep learning has recently been applied to optical music recognition (OMR). However, currently OMR processing from various sheet music images still lacks precision to be widely applicable. Here, we present an MMdA (Measure-based Multimodal deep learning (DL)-driven Assembly) method allowing for end-to-end OMR processing from various images including inclined photo images. Using this method, measures are extracted by a deep learning model, aligned, and resized to be used for inference of given musical symbol components by using multiple deep learning models in sequence or in parallel. Use of each standardized measure enables efficient training of the models and accurate adjustment of five staff lines in each measure. Multiple musical symbol component category models with a small number of feature types can represent a diverse set of notes and other musical symbols including chords. This MMdA method provides a solution to end-to-end OMR processing with precision.
Lagrangian dual framework for conservative neural network solutions of kinetic equations

In this paper, we propose a novel conservative formulation for solving kinetic equations via neural networks. More precisely, we formulate the learning problem as a constrained optimization problem with constraints that represent the physical conservation laws. The constraints are relaxed toward the residual loss function by the Lagrangian duality. By imposing physical conservation properties of the solution as constraints of the learning problem, we demonstrate far more accurate approximations of the solutions in terms of errors and the conservation laws, for the kinetic Fokker-Planck equation and the homogeneous Boltzmann equation.
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search

The idea of Innovation Search, which was initially proposed for data clustering, was recently used for outlier detection. In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points. We study the Innovation Values computed by the Innovation Search algorithm under a quadratic cost function and it is proved that Innovation Values with the new cost function are equivalent to Leverage Scores. This interesting connection is utilized to establish several theoretical guarantees for a Leverage Score based robust PCA method and to design a new robust PCA method. The theoretical results include performance guarantees with different models for the distribution of outliers and the distribution of inliers. In addition, we demonstrate the robustness of the algorithms against the presence of noise. The numerical and theoretical studies indicate that while the presented approach is fast and closed-form, it can outperform most of the existing algorithms.
Another source of mesh dependence in topology optimization

The topology optimization community has regularly employed nonlinear programming (NLP) algorithms from the operations research community. However, these algorithms are implemented in the real vector space $\mathbb{R}^n$ instead of the proper function space where the design variable resides. In this article, we show how the volume fraction variable discretization on non-uniform meshes affects the convergence of $\mathbb{R}^n$ based NLP algorithms. We do so by first summarizing the functional analysis tools necessary to understand why convergence is affected by the mesh. Namely, the distinction between derivative definitions and the role of the mesh-dependent inner product within the NLP algorithm. These tools are then used to make the Globally Convergent Method of Moving Asymptotes (GCMMA), a popular NLP algorithm in the topology optimization community, converge in a mesh independent fashion when starting from the same initial design. We then benchmark our algorithms with three common problems in topology optimization.
Detailed Account of Complexity for Implementation of Quantum Algorithms

Fernando R. Cardoso,1 Daniel Yoshio Akamatsu,1 Eduardo I. Duzzioni,2 Alfredo Jaramillo Palma,3 and Celso J. Villas-Boas1 Departamento de Fı́sica, Universidade Federal de São Carlos, 13565-905 São Carlos, São Paulo, Brazil Departamento de Fı́sica, Universidade Federal de Santa Catarina, Florianópolis, SC, 88040-900, Brazil Escola de Engenharia de São Carlos, Universidade de São Paulo, 13566-590 São Carlos, São Paulo, Brazil
Next generation proton PDFs with deuteron and nuclear uncertainties

As data become more precise, estimating theoretical uncertainties in global PDF determinations is likely to become increasingly necessary to obtain correspondingly precise PDFs. Here we present a soon-to-be-released next generation of global proton PDFs (NNPDF4.0) that include theoretical uncertainties due to the use of heavy nuclear and deuteron data in the fit. We estimate these uncertainties by comparing the values of the nuclear observables computed with the nuclear PDFs against those computed with proton PDFs. For heavy nuclear PDFs we use the nuclear nNNPDF2.0 set, while for deuteron PDFs we develop an iterative procedure to determine proton and deuteron PDFs simultaneously, each including the uncertainties in the other. Accounting for nuclear uncertainties resolves some of the tensions in the global fit of the proton PDFs, especially those between the nuclear data and the extended LHC data set used in NNPDF4.0.
MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing

Hyperdimensional Computing (HDC) is an emerging computational framework that mimics important brain functions by operating over high-dimensional vectors, called hypervectors (HVs). In-memory computing implementations of HDC are desirable since they can significantly reduce data transfer overheads. All existing in-memory HDC platforms consider binary HVs where each dimension is represented with a single bit. However, utilizing multi-bit HVs allows HDC to achieve acceptable accuracies in lower dimensions which in turn leads to higher energy efficiencies. Thus, we propose a highly accurate and efficient multi-bit in-memory HDC inference platform called MIMHD. MIMHD supports multi-bit operations using ferroelectric field-effect transistor (FeFET) crossbar arrays for multiply-and-add and FeFET multi-bit content-addressable memories for associative search. We also introduce a novel hardware-aware retraining framework (HWART) that trains the HDC model to learn to work with MIMHD. For six popular datasets and 4000 dimension HVs, MIMHD using 3-bit (2-bit) precision HVs achieves (i) average accuracies of 92.6% (88.9%) which is 8.5% (4.8%) higher than binary implementations; (ii) 84.1× (78.6×) energy improvement over a GPU, and (iii) 38.4×(34.3×) speedup over a GPU, respectively. The 3-bit MIMHD is 4.3× and 13× faster and more energy-efficient than binary HDC accelerators while achieving similar accuracies.
Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards

Abstract. Climate models are critical tools for developing strategies to manage the risks posed by sea-level rise to coastal communities. While these models are necessary for understanding climate risks, there is a level of uncertainty inherent in each parameter in the models. This model parametric uncertainty leads to uncertainty in future climate risks. Consequently, there is a need to understand how those parameter uncertainties impact our assessment of future climate risks and the efficacy of strategies to manage them. Here, we use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. In this work, we use the Building blocks for Relevant Ice and Climate Knowledge (BRICK) semi-empirical model for sea-level rise. We selected this model because of its balance of computational efficiency and representation of the many different processes that contribute to sea-level rise. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios. The near-term hazards of high-end sea-level rise are driven primarily by thermal expansion, while the longer-term hazards are associated with mass loss from the Antarctic and Greenland ice sheets. Our results highlight the practical importance of considering time-evolving parametric uncertainties when developing strategies to manage future climate risks.
Imitation Learning: Progress, Taxonomies and Opportunities

Imitation learning aims to extract knowledge from human experts’ demonstrations or artiﬁcially created agents in order to replicate their behaviours. Its success has been demonstrated in areas such as video games, autonomous driving, robotic simulations and object manipulation. However, this replicating process could be problematic, such as the performance is highly dependent on the demonstration quality, and most trained agents are limited to perform well in task-speciﬁc environments. In this survey, we provide a systematic review on imitation learning. We ﬁrst introduce the background knowledge from development history and preliminaries, followed by presenting diﬀerent taxonomies within Imitation Learning and key milestones of the ﬁeld. We then detail challenges in learning strategies and present research opportunities with learning policy from suboptimal demonstration, voice instructions and other associated optimization schemes.
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving

To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, and then implement and validate it in autonomous driving under various task scenarios. First, an action-conditioned ensemble model with the ability of uncertainty assessment is established as the virtual environment model. Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance. The developed algorithms are then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. The validation results suggest that the proposed UA-MBRL method surpasses the existing model-based and model-free RL approaches, in terms of learning efficiency and achieved performance. The results also demonstrate the good ability of the proposed method with respect to the adaptiveness and robustness, under various autonomous driving scenarios.
ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search

Recently, deep neural networks have been outperforming conventional machine learning algorithms in many computer vision-related tasks. However, it is not computationally acceptable to implement these models on mobile and IoT devices and the majority of devices are harnessing the cloud computing methodology in which outstanding deep learning models are responsible for analyzing the data on the server. This can bring the communication cost for the devices and make the whole system useless in those times where the communication is not available. In this paper, a new framework for deploying on IoT devices has been proposed which can take advantage of both the cloud and the on-device models by extracting the meta-information from each sample's classification result and evaluating the classification's performance for the necessity of sending the sample to the server. Experimental results show that only 40 percent of the test data should be sent to the server using this technique and the overall accuracy of the framework is 92 percent which improves the accuracy of both client and server models.
The transition between branchial pumping and ram ventilation in fishes: energetic consequences and dependence on water oxygen tension

1. 1. Ram ventilation has been demonstrated in rainbow trout, Salmo gairdneri . The swimming speed eliciting the transition in mode of ventilation increased with declining ambient water oxygen tension (PwOO2). 2. 2. The mode of ventilation of fish swimming at a constant speed could be altered by controlled variations in PwOO2. 3. 3. Oxygen consumption (V·OO2) decreased by 10.2% when rainbow trout, swimming at a constant speed, shifted from active to ram ventilation. This difference can be ascribed to a lowering of the energetic cost of active ventilation as well as improved drag characteristics. 4. 4. Water velocity required for the transition from active to ram ventilation in the resting sharksucker, Echeneis naucrates , increased with decreasing PwOO2. 5. 5. The results show that water oxygen tension is an important stimulus for setting the ventilatory mode in rainbow trout and sharksucker.
Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs

Current popular phishing prevention techniques mainly utilize reactive blocklists, which leave a “window of opportunity” for attackers during which victims are unprotected. One possible approach to shorten this window aims to detect phishing attacks earlier, during website preparation, by monitoring Certificate Transparency (CT) logs. Previous attempts to work with CT log data for phishing classification exist, however they lack evaluations on actual CT log data. In this paper, we present a pipeline that facilitates such evaluations by addressing a number of problems when working with CT log data. The pipeline includes dataset creation, training, and past or live classification of CT logs. Its modular structure makes it possible to easily exchange classifiers or verification sources to support ground truth labeling efforts and classifier comparisons. We test the pipeline on a number of new and existing classifiers, and find a general potential to improve classifiers for this scenario in the future. We publish the source code of the pipeline and the used datasets along with this paper [12], thus making future research in this direction more accessible.
Clustering of check-in sequences using the mixture Markov chain process

This work is devoted to the clustering of check-in sequences from a geosocial network. We used the mixture Markov chain process as a mathematical model for time-dependent types of data. For clustering, we adjusted the Expectation-Maximization (EM) algorithm. As a result, we obtained highly detailed communities (clusters) of users of the now defunct geosocial network, Weeplaces.
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. It follows the previous methods that generate two views of an input graph through data augmentation. However, unlike contrastive methods that focus on instance-level discrimination, we optimize an innovative feature-level objective inspired by classical Canonical Correlation Analysis. Compared with other works, our approach requires none of the parameterized mutual information estimator, additional projector, asymmetric structures, and most importantly, negative samples which can be costly. We show that the new objective essentially 1) aims at discarding augmentation-variant information by learning invariant representations, and 2) can prevent degenerated solutions by decorrelating features in different dimensions. Our theoretical analysis further provides an understanding for the new objective which can be equivalently seen as an instantiation of the Information Bottleneck Principle under the self-supervised setting. Despite its simplicity, our method performs competitively on seven public graph datasets. The code is available at: https://github.com/hengruizhang98/CCA-SSG.
Prospects for Analog Circuits in Deep Networks

Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASIC) designs that implement these algorithms using techniques such as charge sharing circuits and subthreshold transistors, achieve very high power efficiencies. With the recent advances in deep learning algorithms, focus has shifted to hardware digital accelerator designs that implement the prevalent matrix-vector multiplication operations. Power in these designs is usually dominated by the memory access power of off-chip DRAM needed for storing the network weights and activations. Emerging dense non-volatile memory technologies can help to provide on-chip memory and analog circuits can be well suited to implement the needed multiplication-vector operations coupled with in-computing memory approaches. This paper presents abrief review of analog designs that implement various machine learning algorithms. It then presents an outlook for the use ofanalog circuits in low-power deep network accelerators suitable for edge or tiny machine learning applications.