Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Geometry to Detect Grasps in 3D Point Clouds

This paper proposes a new approach to detecting grasp points on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand configurations that are expected to be good grasps. Our key idea is to use knowledge of the geometry of a good grasp to improve detection. First, we use a geometrically necessary condition to sample a large set of high quality grasp hypotheses. We were surprised to find that using simple geometric conditions for detection can result in a relatively high grasp success rate. Second, we use the notion of an antipodal grasp (a standard characterization of a good two fingered grasp) to help us classify these grasp hypotheses. In particular, we generate a large automatically labeled training set that gives us high classification accuracy. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at this http URL
Natural Language Navigation and Spatial Reasoning in Visual Street Environments

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the TOUCHDOWN task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment to a goal position, and then identify in the observed image a location described in natural language to find a hidden object. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. We perform qualitative linguistic analysis, and show that the data displays richer use of spatial reasoning compared to related resources. Empirical analysis shows the data presents an open challenge to existing methods.
An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration

Conversion rate (CVR) prediction is becoming increasingly important in the multi-billion dollar online display advertising industry. It has two major challenges: firstly, the scarce user history data is very complicated and non-linear; secondly, the time delay between the clicks and the corresponding conversions can be very large, e.g., ranging from seconds to weeks. Existing models usually suffer from such scarce and delayed conversion behaviors. In this paper, we propose a novel deep learning framework to tackle the two challenges. Specifically, we extract the pre-trained embedding from impressions/clicks to assist in conversion models and propose an inner/self-attention mechanism to capture the fine-grained personalized product purchase interests from the sequential click data. Besides, to overcome the time-delay issue, we calibrate the delay model by learning dynamic hazard function with the abundant post-click data more in line with the real distribution. Empirical experiments with real-world user behavior data prove the effectiveness of the proposed method.
GSSPs, global stratigraphy and correlation

Abstract Procedures used to define an international chronostratigraphic stage boundary and to locate and ratify a Global Boundary Stratotype Section and Point (GSSP) are outlined. A majority of current GSSPs use biostratigraphic data as primary markers with no reference to any physico-chemical markers, despite the International Subcommission on Stratigraphic Classification (ISSC) suggestion that such markers should be included if possible. It is argued that such definitions will not produce the high-precision Phanerozoic time scale necessary to understand such phenomena as pre-Pleistocene ice ages and global climate change. It is strongly recommended that all GSSPs should have physico-chemical markers as an integral part of their guiding criteria, and where such markers cannot be found, the GSSP should be relocated. The methods and approach embodied in oceanic stratigraphy – coring, logging, analysing and archiving of drill sites by numerous experts using a wide range of methods – could usefully serve as a scientific model for the analysis and archiving of GSSPs, all of which are on the present-day continents. The incorporation of many more stratigraphic sections into GSSP studies, the application of physico-chemical methods, and the replacement of old U–Pb dates by newer CA-TIMS U–Pb dates, together with the use of constrained optimization (CONOP) programs that produce a calendar of events from many sections, should lead to much more precise timescales for pre-Cenozoic time than are currently available.
Verifier Ver Prover Prv Challenge c Application Module A Response r Measure CFG Paths : H ( G ) Measurement DB Application Module A Execute : Exec

Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices. It allows a trusted party (verifier) to learn the state of a remote, and potentially malware-infected, device (prover). Most existing approaches are static in nature and only check whether benign software is initially loaded on the prover. However, they are vulnerable to runtime attacks that hijack the application’s control or data flow, e.g., via return-oriented programming or data-oriented exploits. As a concrete step towards more comprehensive runtime remote attestation, we present the design and implementation of ControlFLow ATtestation (C-FLAT) that enables remote attestation of an application’s control-flow path, without requiring the source code. We describe a full prototype implementation of C-FLAT on Raspberry Pi using its ARM TrustZone hardware security extensions. We evaluate C-FLAT’s performance using a real-world embedded (cyber-physical) application, and demonstrate its efficacy against control-flow hijacking attacks.
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, ﬁnding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically ‘cut’ object instances and ‘paste’ them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data.
Research priorities for robust and beneﬁcial artiﬁcial intelligence

Executive Summary: Success in the quest for artiﬁcial intelligence has the potential to bring unprecedented beneﬁts to humanity, and it is therefore worthwhile to research how to maximize these beneﬁts while avoiding potential pitfalls. This document gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneﬁcial.
Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present T HESEUS , an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that T HESEUS will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. T HESEUS is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level. DOI: 10.1103/PhysRevX.11. 031044
Learning Less is More – 6 D Camera Localization via 3 D Surface Regression – Supplementary Materials –

Our FCN [3] network architecture (see also Fig. 1) takes an image of 640 × 480 px as input. We re-scale larger images to 480 px height. Should an image be wider than 640 px after re-scaling, we crop it centrally to 640 px width. After scene coordinate prediction, we sample n = 256 hypotheses using random 4-tuples of points and the algorithm of [1]. We reject hypotheses where the reprojection error of the corresponding 4-tuple is larger than the inlier threshold τ , and sample again. We set the inlier threshold τ = 10px for all scenes. For the soft inlier count (Eq. 5 of the main paper) we use a softness factor β = 0.5. We refine the selected hypothesis until convergence or for a maximum of 100 iterations.
Sparsistency and Rates of Convergence in Large Covariance Matrices Estimation

This paper studies the sparsistency, rates of convergence, and asymptotic normality for estimating sparse covariance matrices based on penalized likelihood with non-concave penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the covariance matrix, or its inverse or its Cholesky decomposition. We study these three sparsity exploration problems under a unified framework with a general penalty function. We show that the rates of convergence for these problems under the Frobenius norm are of order (sn log pn/n) , where sn is the number of nonsparse elements, pn is the size of the covariance matrix and n is the sample size. This explicitly spells out the contribution of high-dimensionality is merely of a logarithmic factor. The biases of the estimators using different penalty functions are explicitly obtained. As a result, for the L1-penalty, to obtain the sparsistency and optimal rate of convergence, the non-sparsity rates must be low: sn = O(p 1/2 n ) among O(pn) parameters, for estimating sparse covariance matrix, or sparse precision matrix or sparse Cholesky factor and sn = O(1) for estimating sparse correlation matrix or its inverse, where sn is the number of the non-sparse elements on the off-diagonal entries. On the other hand, using the SCAD or hard-thresholding penalty functions, there are no such a restriction.
VIPER: Volume Invariant Position-based Elastic Rods

We extend the formulation of position-based rods to include elastic volumetric deformations. We achieve this by introducing an additional degree of freedom per vertex -- isotropic scale (and its velocity). Including scale enriches the space of possible deformations, allowing the simulation of volumetric effects, such as a reduction in cross-sectional area when a rod is stretched. We rigorously derive the continuous formulation of its elastic energy potentials, and hence its associated position-based dynamics (PBD) updates to realize this model, enabling the simulation of up to 26000 DOFs at 140 Hz in our GPU implementation. We further show how rods can provide a compact alternative to tetrahedral meshes for the representation of complex muscle deformations, as well as providing a convenient representation for collision detection. This is achieved by modeling a muscle as a bundle of rods, for which we also introduce a technique to automatically convert a muscle surface mesh into a rods-bundle. Finally, we show how rods and/or bundles can be skinned to a surface mesh to drive its deformation, resulting in an alternative to cages for real-time volumetric deformation.
Poisson Formula for Resonances in Even Dimensions

We consider scattering by an abstract compactly supported perturbation in R^n. To include the traditional cases of potential, obstacle and metric scattering without going into their particular nature we adopt the "black box" formalism developed jointly with Sjostrand [23]. It is quite likely that one could extend the results presented here to the case of non-compactly supported perturbation as well - see [21] for a natural generalization of "black box" perturbations.
Integrated Access and Backhaul in 5G mmWave Networks: Potential and Challenges

IAB is being considered as a means to reduce the deployment costs of ultra-dense 5G mmWave networks, using wireless backhaul links to relay the access traffic. In this work we describe the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base station, it is still necessary to validate the IAB performance in realistic scenarios. In this article we demonstrate the cell edge throughput advantage offered by IAB using endto- end system-level simulations. We also highlight some research challenges for IAB that will require further investigations.
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition - supplemental

A rotation matrix in d-dimensional space has d-by-d parameters, which is infeasible to learn. And as it only has d − 1 degrees of freedom, further constraints have to be implemented if the matrix is directly learned. To avoid these difficulties, we define the structure parameters as ddimensional vectors, and for each vector, the rotation matrix from a basic vector [1, 0, 0, . , 0] to the give vector is used as the rotation on class weights. In a general case, given two vectors x and y, we want to find the rotation matrix R from x to y. One way to do this is to find the plane spanned by x and y, and then with respect to this, consider the 2D rotation on the plane. With Gram–Schmidt process, we find the orthonormal basis as
Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision

Due to space limitation in the main paper, we present additional experimental results in this supplementary material. We show additional results for synthetic dataset, and real-world applications, such as background modeling, High Dynamic Range Imaging, outlier removal for photometric stereo and batch image alignment as well as proof of the main result, Partial Singular Value Thresholding (PSVT) operator. All the parameter not mentioned are same with the parameters used in the main paper.
Octree Generating Networks : Efficient Convolutional Architectures for High-resolution

In the main paper we have shown that with a practical architecture our networks scale much better than their dense counterparts both in terms of memory consumption and computation time. The numbers were obtained for the ”houses” scene from the BlendSwap dataset. In order to further study this matter, we have designed a set of slim decoder networks that fit on a GPU in every resolution, including 512, both with an OGN and a dense representation. The architectures of those networks are similar to those from Table 7, but with only 1 channel in every convolutional layer, and a single fully-connected layer with 64 units in the encoder. The resulting measurements are shown in Figure 1 for memory consumption and Figure 2 for runtime. To precisely quantify the scaling, we subtracted the constant amount of memory reserved on a GPU by caffe (190 MB) from all numbers. Both plots are displayed in the log-log scale, i.e., functions from the family y = ax are straight lines. The 32 64 128 256 512 0.01 0.1 1 10
3D-BEVIS: Birds-Eye-View Instance Segmentation

Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird’s-eye view representation.
Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction

—Learning to control a robot commonly requires mapping between robot states and camera images, where conventional deep vision models require large training dataset. Existing visual attention models, such as Deep Spatial Autoencoders, have improved the data-efﬁciency by training the model to selectively extract only the task relevant image area. However, since the models are unable to select attention targets on demand, the diversity of trainable tasks are limited. This paper proposed a novel Key-Query-Value formulated visual attention model which can be guided to a certain attention target. The model creates an attention heatmap from Key and Query, and selectively extracts the attended data represented in Value. Such structure is capable of incorporating external inputs to create the Query, which will be trained to represent the target objects. The separation of Query creation improved the model’s ﬂexibility, enabling to simultaneously obtain and switch between multiple targets in a top-down manner. The proposed model is experimented on a simulator and a real-world environment, showing better performance compared to existing end-to-end robot vision models. The results of real-world experiments indicated the model’s high scalability and extendiblity on robot controlling tasks.
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation

Our goal was to reproduce the original paper’s central claim that projecting away the word-frequency direction(s) in 3 word embeddings improves the de-biasing performance of the well-known Hard Debias algorithm. The objectives were 4 to first verify that such word-frequency direction(s) exist and then verify removing these direction(s) decreases bias 5 without significantly affecting the embeddings’ utility. 6
NOMCLUST: AN R PACKAGE FOR HIERARCHICAL CLUSTERING OF OBJECTS CHARACTERIZED BY NOMINAL VARIABLES

Clustering of objects characterized by nominal (categorical) variables is not still sufficiently solved in software systems. Most of them provide only hierarchical cluster analysis with one basic similarity measure for nominal data, the simple matching coefficient, which provides worse results than clustering based on some of recently proposed measures. In this paper, we introduce the nomclust R package, which completely covers hierarchical clustering of objects characterized by nominal variables from a proximity matrix computation to final clusters evaluation. It enables to choose among 11 similarity measures, three linkage methods of hierarchical clustering, and uses six evaluation criteria based on the mutability and the entropy. Some of the criteria were constructed for comparison of different cluster approaches and some for determining the optimal number of clusters. The package contains both the main function, which covers the whole clustering process, and a set of subsidiary functions, which allow computing only a part of clustering process, e.g. a proximity matrix, or evaluation of given cluster solution. We illustrate the methodology of this package on a small dataset.
Harnessing disordered quantum dynamics for machine learning

Keisuke Fujii 2, ∗ and Kohei Nakajima 3, 4 The Hakubi Center for Advanced Research, Kyoto University, Yoshida-Ushinomiya-cho, Sakyo-ku, Kyoto 606-8302, Japan Department of Physics, Graduate School of Science, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501, Japan JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan (Dated: February 29, 2016)
A Joint Exponential Mechanism for Differentially Private Top-k Set

We present a novel differentially private algorithm for releasing the set of k elements with the highest counts from a data domain of d elements. We deﬁne a “joint” instance of the exponential mechanism (EM) whose output space consists of all O ( d k ) size-k subsets; yet, we are able to show how to sample from this EM in only time ˜ O ( dk 3 ) . Experiments suggest that this joint approach can yield utility improvements over the existing state of the art for small problem sizes
Scope and Construction of a Gas and Oil Atlas Series of the Gulf of Mexico: Examples from Texas Offshore Lower Miocene Plays

ABSTRACT An atlas series about the offshore northern Gulf of Mexico will group gas and oil reservoirs into subregional plays and will display reservoir data on a computerized geographical information system. The atlas series will provide critically compiled reservoir engineering data to help the private sector explore and develop hydrocarbons and to help the public sector analyze the hydrocarbon endowment in this basin. In this report, we cover aspects of the play-analysis procedure and provide specific examples of lower Miocene plays from the upper Texas coast and Federal Outer Continental Shelf (OCS). Play analysis emphasizes using broad classes of structural style, depositional style and environments, and defining attributes to group reservoirs into plays. To date, we have identified 4 Oligocene and 25 Miocene plays in Texas State offshore waters and 115 plays in the Federal OCS. Texas State offshore plays are gas prone (cumulative production 3.7 Tcf) and are preferentially trapped in rollover anticlines. Miocene plays include submarine-fan Lenticulina sandstones; progradational Discorbis b. , Siphonina d. , Marginulina a. , and Lenticulina sandstones; transgressive sandstones associated with a barrier-bar system in the Matagorda Area; and transgressive Amphistegina B sandstones. Particularly productive gas-prone plays are progradational Siphonina d. deltas in the High Island Area and progradational Marginulina a. shelf and deltaic sandstones in the Matagorda Island and Brazos Areas.