Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates of about 70% are aligned.
Relaxed Paxos: quorum intersection revisited (again)

Distributed consensus, the ability to reach agreement in the face of failures, is a fundamental primitive for constructing reliable distributed systems. The Paxos algorithm is synonymous with consensus and widely utilized in production. Paxos uses two phases: phase one and phase two, each requiring a quorum of acceptors, to reach consensus during a round of the protocol. Traditionally, Paxos requires that all quorums, regardless of phase or round, intersect and majorities are often used for this purpose. Flexible Paxos proved that it is only necessary for phase one quorum of a given round to intersect with the phase two quorums of all previous rounds. In this paper, we re-examine how Paxos approaches the problem of consensus. We look again at quorum intersection in Flexible Paxos and observe that quorum intersection can be safely weakened further. Most notably, we observe that if a proposer learns that a value was proposed in some previous round then its phase one no longer needs to intersect with the phase two quorums from that round or from any previous rounds. Furthermore, in order to provide an intuitive explanation of our results, we propose a novel abstraction for reasoning about Paxos which utilizes write-once registers.
Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

As one of the fundamental graph operations, reachability queries processing has been extensively studied during the past decades. Many approaches followed the line of designing 2-hop labels to make acceleration. Considering that the index size cannot be bounded when using all nodes to construct 2-hop labels, researchers proposed to use a part of important nodes to construct 2-hop labels (partial 2-hop labels) to cover as much reachability information as possible. Then, we may achieve better query performance with limited index size and index construction time. However, partial 2-hop labels do not always perform well on different graphs. In this paper, we focus on the problem of how to efficiently compute reachability ratio, such that to help users determine whether partial 2-hop labels should be used to answer reachability queries for the given graph. Intuitively, reachability ratio denotes the ratio of the number of reachable queries that can be answered by partial 2-hop labels over the total number of reachable queries involved in the given graph. We discuss the difficulties of reachability ratio computation, and propose an incremental-partition algorithm for reachability ratio computation. We show by rich experimental results that our algorithm can efficiently get the result of reachability ratio, and show how the overall query performance is affected by different partial 2-hop labels. Based on the experimental results, we give out our findings on whether partial 2-hop labels should be used to the given graph for reachability queries processing.
Least Square Estimation Network for Depth Completion

—Depth completion is a fundamental task in computer vision and robotics research. Many previous works complete the dense depth map with neural networks directly but most of them are non-interpretable and can not generalize to different situations well. In this paper, we propose an effective image representation method for depth completion tasks. The input of our system is a monocular camera frame and the synchronous sparse depth map. The output of our system is a dense per-pixel depth map of the frame. First we use a neural network to transform each pixel into a feature vector, which we call base functions. Then we pick out the known pixels’ base functions and their depth values. We use a linear least square algorithm to ﬁt the base functions and the depth values. Then we get the weights estimated from the least square algorithm. Finally, we apply the weights to the whole image and predict the ﬁnal depth map. Our method is interpretable so it can generalize well. Experiments show that our results beat the state-of-the-art on NYU-Depth-V2 dataset [19] both in accuracy and runtime. Moreover, experiments show that our method can generalize well on different numbers of sparse points and different datasets.
Metallicity, Ionization Parameter, and Pressure Variations of H ii Regions in the TYPHOON Spiral Galaxies: NGC 1566, NGC 2835, NGC 3521, NGC 5068, NGC 5236, and NGC 7793

We present a spatially resolved H ii region study of the gas-phase metallicity, ionization parameter, and interstellar medium (ISM) pressure maps of six local star-forming and face-on spiral galaxies from the TYPHOON program. Self-consistent metallicity, ionization parameter, and pressure maps are calculated simultaneously through an iterative process to provide useful measures of the local chemical abundance and its relation to localized ISM properties. We constrain the presence of azimuthal variations in metallicity by measuring the residual metallicity offset Δ(O/H) after subtracting the linear fits to the radial metallicity profiles. We, however, find weak evidence of azimuthal variations in most of the galaxies, with small (mean 0.03 dex) scatter. The galaxies instead reveal that H ii regions with enhanced and reduced abundances are found distributed throughout the disk. While the spiral pattern plays a role in organizing the ISM, it alone does not establish the relatively uniform azimuthal variations we observe. Differences in the metal abundances are more likely driven by the strong correlations with the local physical conditions. We find a strong and positive correlation between the ionization parameter and the local abundances as measured by the relative metallicity offset Δ(O/H), indicating a tight relationship between local physical conditions and their localized enrichment of the ISM. Additionally, we demonstrate the impact of unresolved observations on the measured ISM properties by rebinning the data cubes to simulate low-resolution (1 kpc) observations, typical of large IFU surveys. We find that the ionization parameter and ISM pressure diagnostics are impacted by the loss of resolution such that their measured values are larger relative to the measured values on sub-H ii region scales.
Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality

Consider a robot operating in an uncertain environment with stochastic, dynamic obstacles. Despite the clear benefits for trajectory optimization, it is often hard to keep track of each obstacle at every time step due to sensing and hardware limitations. We introduce the <inline-formula><tex-math notation="LaTeX">$\mathtt {Safely}{}$</tex-math></inline-formula> motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot. We perform the motion planning using sequential quadratic programming, and prescribe obstacles to sense based on a novel connection between the duality information associated with the convex subproblems and the effect of uncertainty reduction through Kalman filter updates. We guarantee safety by ensuring that the probability of the robot colliding with any of the obstacles is below a prescribed threshold at every time step of the planned robot trajectory. We demonstrate the efficacy of the <inline-formula><tex-math notation="LaTeX">$\mathtt {Safely}{}$</tex-math></inline-formula> motion planner through software and hardware experiments.
A Jordan-Chevalley decomposition beyond algebraic groups

A generalization of the Jordan-Chevalley decomposition of linear algebraic groups to definable groups in o-minimal structures is considered. As an application, we give a description of the commutator subgroup of a definable group G, and show that when G has definable Levi subgroups, [G, G] is definable. Along the way, we identify crucial central subgroups, study disconnected groups, definable p-groups and 0-groups, abstractly compact subgroups, the Weyl group, and discuss a couple of other structure decompositions: 1) a compact-torsionfree decomposition and 2) a Levi decomposition. It turns out that definable p-groups are solvable, like finite p-groups, but they are not necessarily nilpotent.
Semantic Segmentation in Art Paintings

Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task because of a much larger diversity in colors, textures, and shapes and because there are no ground truth annotations available for segmentation. We propose an unsupervised method for semantic segmentation of paintings using domain adaptation. Our approach creates a training set of pseudo‐paintings in specific artistic styles by using style‐transfer on the PASCAL VOC 2012 dataset, and then applies domain confusion between PASCAL VOC 2012 and real paintings. These two steps build on a new dataset we gathered called DRAM (Diverse Realism in Art Movements) composed of figurative art paintings from four movements, which are highly diverse in pattern, color, and geometry. To segment new paintings, we present a composite multi‐domain adaptation method that trains on each sub‐domain separately and composes their solutions during inference time. Our method provides better segmentation results not only on the specific artistic movements of DRAM, but also on other, unseen ones. We compare our approach to alternative methods and show applications of semantic segmentation in art paintings. The code and models for our approach are publicly available at: https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.
Secure and Authorized Client-to-Client Communication for LwM2M

Constrained devices on the Internet of Things (IoT) continuously produce and consume data. LwM2M manages millions of these devices in a server-centric architecture, which challenges edge net-works with expensive uplinks and time-sensitive use cases. In this paper, we contribute two LwM2M extensions to enable client-to-client (C2C) communication: (i) an authorization mechanism for clients, and (ii) an extended management interface to allow secure C2C access to resources. We analyse the security properties of the proposed extensions and show that they are compliant with LwM2M security requirements. Our performance evaluation on off-the-shelf IoT hardware reveals that C2C communication out-performs server-centric deployments. First, LwM2M deployments with edge C2C communication yield a ≈ 90% faster notification delivery and ≈ 8 times higher throughput compared to common server-centric scenarios, while keeping a small memory overhead of ≈ 8%. Second, in server-centric communication, the delivery rate degrades when resource update intervals drop below 100 ms.
Google SketchUp: The Missing Manual

Google SketchUp simplifies 3-D modelling, is free on the web, and helps you create 3-D models of everything from your garden to a large building. Google SketchUp: The Missing Manual shows you how to make the most of the program's tools and how to apply them to presentations and online sharing. The book introduces you to 3-D modelling, creating and animating a complicated model building, adding realistic material shading, and placing your model in Google Earth. Using the Push/Pull Tool If youve ever seen those camping tents that spring from flat to fully-formed, you have an idea how SketchUps Push/Pull tool works. You start with a flat 2D drawing and you stretch it to whatever length you want. Push/Pull works its magic on any shape that forms a face. In these steps, you use the Push/Pull tool to alter a simple box. 1.Use the Rectangle tool to draw a box, and then click the Iso button or choose Camera--Standard Views--Iso. You may even want to use the Orbit tool, to get a good view of three faces of the box, as shown below. Each edge on the box has two endpoints and a midpoint. To find the midpoint, move the cursor slowly along the edge. The cursor will snap to the midpoint when it gets close and you see a tool tip that says Midpoint. 2.Select the Rectangle tool and move the cursor over the different faces and points of the box. When the cursor is over a face, a tool tip says On Face. When the cursor is over an end point or midpoint, the marker at the tip of the pencil changes color and you see appropriate tool tip messages. In the next step, you use an end point and a midpoint to draw a rectangle on the top surface of the box. 3.With the Rectangle tool, click on the front, right corner and then move to the midpoint on the back of the box, as shown below. After you create the rectangle, your box has an edge down the middle that divides it in half. The top of the box now has two separate faces. Start drawing from the front corner and then stretch the rectangle out to the midpoint on the backside. 4.With the Push/Pull tool (P), click the face on the left and move the mouse forward and backward. As you move the mouse, the model changes shape. When you pull up with the Push/Pull tool, youre adding geometry or adding volume to your simple box. When you push it down, youre subtracting geometry. 5.Pull the face up and then click the mouse button. This leaves the face pulled up, and creates a new vertical face in the model. 6.With Push/Pull, push the vertical face back. As you push the face as shown below, you subtract volume from the model, making it smaller. 7.Continue to push the face until you reach the back edge of the box and then click. The face snaps to the back edge and then when you click, the face disappears. Your box looks as it did when you started this exercise. Pushing a face back shrinks an object, sometimes referred to as removing geometry or reducing its volume. Pushing this face all the way back until it reaches the opposite face removes it entirely.
Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods

Counterfactual fairness methods address the question: How would the prediction change if the sensitive identity attributes referenced in the text instance were different? These methods are entirely based on generating counterfactuals for the given training and test set instances. Counterfactual instances are commonly prepared by replacing sensitive identity terms, i.e., the identity terms present in the instance are replaced with other identity terms that fall under the same sensitive category. Therefore, the efficacy of these methods depends heavily on the quality and comprehensiveness of identity pairs. In this paper, we offer a two-step data augmentation process where (1) the former stage consists of a novel method for preparing a comprehensive list of identity pairs with word embeddings, and (2) the latter consists of leveraging prepared identity pairs list to enhance the training instances by applying three simple operations (namely identity pair replacement, identity term blindness, and identity pair swap). We empirically show that the two-stage augmentation process leads to diverse identity pairs and an enhanced training set, with an improved counterfactual token-based fairness metric score on two well-known text classification tasks.
Detecting data-driven robust statistical arbitrage strategies with deep neural networks

We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets. Robust statistical arbitrage strategies refer to trading strategies that enable profitable trading under model ambiguity. The presented novel methodology allows to consider a large amount of underlying securities simultaneously and does not depend on the identification of cointegrated pairs of assets, hence it is applicable on high-dimensional financial markets or in markets where classical pairs trading approaches fail. Moreover, we provide a method to build an ambiguity set of admissible probability measures that can be derived from observed market data. Thus, the approach can be considered as being model-free and entirely data-driven. We showcase the applicability of our method by providing empirical investigations with highly profitable trading performances even in 50 dimensions, during financial crises, and when the cointegration relationship between asset pairs stops to persist.
Emotion Regulation and Dynamics of Moral Concerns During the Early COVID-19 Pandemic

The COVID-19 pandemic has upended daily life around the globe, posing a threat to public health. Intuitively, we expect that surging cases and deaths would lead to fear, distress and other negative emotions. However, using state-of-the-art methods to measure sentiment, emotions, and moral concerns in social media messages posted in the early stage of the pandemic, we see a counter-intuitive rise in positive affect. We hypothesize that the increase of positivity is associated with a decrease of uncertainty and emotion regulation. Finally, we identify a partisan divide in moral and emotional reactions that emerged after the first US death. Overall, these results show how collective emotional states have changed since the pandemic began, and how social media can provide a useful tool to understand, and even regulate, diverse patterns underlying human affect.
Verification of Bitcoin's Smart Contracts in Agda using Weakest Preconditions for Access Control

This paper addresses the veriﬁcation of Bitcoin smart contracts using the interactive theorem prover Agda. It focuses on two standard smart contracts that govern the distribution of Bitcoins, Pay to Public Key Hash (P2PKH) and Pay to Multisig (P2MS) . Both are written in Bitcoin’s low-level language script , and provide the security property of access control to the distribution of Bitcoins. The paper introduces an operational semantics of the script commands used in P2PKH and P2MS, and formalises it in the Agda proof assistant using Hoare triples. It advocates weakest preconditions in the context of Hoare triples as the appropriate notion for verifying access control. Two methodologies for obtaining human-readable weakest preconditions are discussed in order to close the validation gap between user requirements and formal speciﬁcation of smart contracts: (1) a step-by-step approach, which works backwards instruction by instruction through a script, sometimes stepping over several instructions in one go; (2) symbolic execution of the code and translation into a nested case distinction, which allows to read oﬀ weakest preconditions as the disjunction of accepting paths. A syntax for equational reasoning with Hoare Triples is deﬁned in order to formalise those approaches in Agda.
A Small Gain Analysis of Single Timescale Actor Critic

We consider a version of actor-critic which uses proportional step-sizes and only one critic update with a single sample from the stationary distribution per actor step. We provide an analysis of this method using the small-gain theorem. Specifically, we prove that this method can be used to find a stationary point, and that the resulting sample complexity improves the state of the art for actor-critic methods to $O \left(\mu^{-2} \epsilon^{-2} \right)$ to find an $\epsilon$-approximate stationary point where $\mu$ is the condition number associated with the critic.
Rib Suppression in Digital Chest Tomosynthesis

Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles. However, under the limited angle scenario, DCT contains strong artifacts caused by the presence of ribs, jamming the imaging quality of the lung area. Recently, great progress has been achieved for rib suppression in a single X-ray image, to reveal a clearer lung texture. We firstly extend the rib suppression problem to the 3D case at the software level. We propose a $\textbf{T}$omosynthesis $\textbf{RI}$b Su$\textbf{P}$pression and $\textbf{L}$ung $\textbf{E}$nhancement $\textbf{Net}$work (TRIPLE-Net) to model the 3D rib component and provide a rib-free DCT. TRIPLE-Net takes the advantages from both 2D and 3D domains, which model the ribs in DCT with the exact FBP procedure and 3D depth information, respectively. The experiments on simulated datasets and clinical data have shown the effectiveness of TRIPLE-Net to preserve lung details as well as improve the imaging quality of pulmonary diseases. Finally, an expert user study confirms our findings.
Sparsifying Transformer Models with Trainable Representation Pooling

We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-k operator.Our experiments on a challenging long document summarization task show that even our simple baseline performs comparably to the current SOTA, and with trainable pooling we can retain its top quality, while being 1.8\times faster during training, 4.5\times faster during inference, and up to 13\times more computationally efficient in the decoder.
DA-LMR: A Robust Lane Marking Representation for Data Association

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1 % in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.