nils-herrmann/clean_scirepeval huggingface dataset:
Composite Shape Modeling via Latent Space Factorization

We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic structure of the shape collection translates into a data-dependent sub-space factorization, and where shape composition and decomposition become simple linear operations on the embedding coordinates. We further propose to model shape assembly using an explicit learned part deformation module, which utilizes a 3D spatial transformer network to perform an in-network volumetric grid deformation, and which allows us to train the whole system end-to-end. The resulting network allows us to perform part-level shape manipulation, unattainable by existing approaches. Our extensive ablation study, comparison to baseline methods and qualitative analysis demonstrate the improved performance of the proposed method.
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to ``instance-level'' 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a \textbf{Normalized Object Coordinate Space (NOCS)}---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new context-aware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.
A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials

In machine learning, we are given a dataset of the form {(xj,yj)}j=1M, drawn as i.i.d. samples from an unknown probability distribution μ; the marginal distribution for the xj's being μ*, and the marginals of the kth class μk*(x) possibly overlapping. We address the problem of detecting, with a high degree of certainty, for which x we have μk*(x)>μi*(x) for all i ≠ k. We propose that rather than using a positive kernel such as the Gaussian for estimation of these measures, using a non-positive kernel that preserves a large number of moments of these measures yields an optimal approximation. We use multi-variate Hermite polynomials for this purpose, and prove optimal and local approximation results in a supremum norm in a probabilistic sense. Together with a permutation test developed with the same kernel, we prove that the kernel estimator serves as a “witness function” in classification problems. Thus, if the value of this estimator at a point x exceeds a certain threshold, then the point is reliably in a certain class. This approach can be used to modify pretrained algorithms, such as neural networks or nonlinear dimension reduction techniques, to identify in-class vs out-of-class regions for the purposes of generative models, classification uncertainty, or finding robust centroids. This fact is demonstrated in a number of real world data sets including MNIST, CIFAR10, Science News documents, and LaLonde data sets.
The Square Root Rule for Adaptive Importance Sampling

In adaptive importance sampling and other contexts, we have K > 1 unbiased and uncorrelated estimates μ^k of a common quantity μ. The optimal unbiased linear combination weights them inversely to their variances, but those weights are unknown and hard to estimate. A simple deterministic square root rule based on a working model that Var(μ^k) ∝ k−1/2 gives an unbiased estimate of μ that is nearly optimal under a wide range of alternative variance patterns. We show that if Var(μ^k)∝ k−y for an unknown rate parameter y∈[0,1], then the square root rule yields the optimal variance rate with a constant that is too large by at most 9/8 for any 0 ⩽ y⩽ 1 and any number K of estimates. Numerical work shows that rule is similarly robust to some other patterns with mildly decreasing variance as k increases.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation

Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS for semantic image segmentation. Existing works often focus on searching the repeatable cell structure, while hand-designing the outer network structure that controls the spatial resolution changes. This choice simplifies the search space, but becomes increasingly problematic for dense image prediction which exhibits a lot more network level architectural variations. Therefore, we propose to search the network level structure in addition to the cell level structure, which forms a hierarchical architecture search space. We present a network level search space that includes many popular designs, and develop a formulation that allows efficient gradient-based architecture search (3 P100 GPU days on Cityscapes images). We demonstrate the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets. Auto-DeepLab, our architecture searched specifically for semantic image segmentation, attains state-of-the-art performance without any ImageNet pretraining.
Estimating population average treatment effects from experiments with noncompliance

Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart when compliance is relatively low and can be predicted by observed covariates. We apply the method to evaluate the effect of Medicaid coverage on health care use for a target population of adults who may benefit from expansions to the Medicaid program. We draw RCT data from the Oregon Health Insurance Experiment, where less than one-third of those randomly selected to receive Medicaid benefits actually enrolled.
Secure and Computationally-Efficient Cryptographic Primitive based on Cellular Automation

The cellular automaton generator (CAG), a random number generator based on the one-dimensional cellular automaton (CA), is presented. Three procedures of secure implementation using the CAG are proposed and discussed. Implementations are very efficient in a wide range of hardware and software scenarios. That includes the advanced application of internet of things (IoT) and cyber-physical systems, which are both needed for computationally efficient cryptographic primitives. Furthermore, the proposed primitive is inherently resistant against the side-channel attack (SCA), where many currently available ciphers, such as the advanced encryption standard (AES), require additional hardware or software effort to prevent the SCA line of attack.
Sentence Rewriting for Semantic Parsing

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form. Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1-N mismatch and a template-based method for N-1 mismatch. We evaluate our entence rewriting based semantic parser on the benchmark semantic parsing dataset -- WEBQUESTIONS. Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction

This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications. Additionally, we also establish an Open Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their brain waves and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

3D object classification using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. We present a preliminary evaluation of adversarial attacks on 3D point cloud classifiers by evaluating adversarial attacks that were proposed for 2D images, and extending those attacks to reduce the perceptibility of the perturbations in 3D space. We also show the effectiveness of simple defenses against those attacks. Finally, we attempt to explain the effectiveness of the defenses through the intrinsic structures of both the point clouds and the neural networks. Overall, we find that 3D point cloud classifiers are weak to adversarial attacks, but they are also more easily defensible compared to 2D image classifiers. Our investigation will provide the groundwork for future studies on improving the robustness of deep neural networks that handle 3D data.
EGL formula for DT/PT theory of local curves

In this note we prove an integral formula for the bare one-leg PT vertex with descendents. 
The formula follows from the PT version of Ellingsrud-Gottsche-Lehn formula that is explained here. We apply the integral formula to obtain an elementary proof of rationality of one-leg capped PT vertex with descendents. 
We also obtain an integral formula for degree zero DT invariants with descendents. Finally we propose an explicit non-equivariant DT/PT correspondence as well as one descendent insertion fully-equivariant DT/PT formula.
On the Bailout Dividend Problem for Spectrally Negative Markov Additive Models

This paper studies the bail-out optimal dividend problem with regime switching under the constraint that the cumulative dividend strategy is absolutely continuous. We confirm the optimality of the regime-modulated refraction-reflection strategy when the underlying risk model follows a general spectrally negative Markov additive process. To verify the conjecture of a barrier type optimal control, we first introduce and study an auxiliary problem with the final payoff at an exponential terminal time and characterize the optimal threshold explicitly using fluctuation identities of the refracted-reflected Levy process. Second, we transform the problem with regime-switching into an equivalent local optimization problem with a final payoff up to the first regime switching time. The refraction-reflection strategy with regime-modulated thresholds can be shown as optimal by using results in the first step and some fixed point arguments for auxiliary recursive iterations.
Mean–variance portfolio selection under partial information with drift uncertainty

In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.
Image Transformation can make Neural Networks more robust against Adversarial Examples

Neural networks are being applied in many tasks related to IoT with encouraging results. For example, neural networks can precisely detect human, objects and animal via surveillance camera for security purpose. However, neural networks have been recently found vulnerable to well-designed input samples that called adversarial examples. Such issue causes neural networks to misclassify adversarial examples that are imperceptible to humans. We found giving a rotation to an adversarial example image can defeat the effect of adversarial examples. Using MNIST number images as the original images, we first generated adversarial examples to neural network recognizer, which was completely fooled by the forged examples. Then we rotated the adversarial image and gave them to the recognizer to find the recognizer to regain the correct recognition. Thus, we empirically confirmed rotation to images can protect pattern recognizer based on neural networks from adversarial example attacks.
Dynamic Multiparty Authentication of Data Analytics Services Within Cloud Environments

Approaches to the provision of data analytics for businesses offer methods to analyse and model data, enabling informed decision making to improve business performance and profitability. Typically, analytics processing is an intensive task and the demand for business insight, on-demand, means that organisations make use of elastic cloud provisioned resources to host such services. However, within the shared domains of multi-tenant cloud computing, business data and models are exposed to greater security threats and compromised privacy, since an unauthorised user may be able to gain access to highly sensitive, consolidated business-critical information. Business analytics processes are often composed from orchestrated, collaborating services, which are consumed by users from multiple cloud systems (in different security realms), which need to be engaged dynamically at runtime. If heterogeneous cloud systems located in different security realms do not have direct authentication relationships, then it is a considerable technical challenge to enable secure collaboration. In order to address this security challenge, a new authentication framework is required to establish trust amongst business analytics service instances and users by distributing a common session secret to all participants of a session. We address this challenge by designing and implementing a secure multiparty authentication framework for dynamic interaction, for the scenario where members of different security realms express a need to access orchestrated services. This novel framework exploits the relationship of trust between session members in different security realms, to enable a user to obtain security credentials that access cloud resources in a remote realm. The mechanism assists cloud session users to authenticate their session membership, thereby improving the performance of authentication processes within multiparty sessions. We see applicability of this framework beyond multiple cloud infrastructure, to that of any scenario where multiple security realms has the potential to exist, such as the emerging Internet of Things (IoT).
A Secure Connectivity Model for Internet of Things Analytics Service Delivery

Wide scale interest and adoption of Internet of Things (IoT) technologies is fuelling innovation in the way individuals and even machines can interact to exchange knowledge. One area of particular interest is that of analytics. Ever-decreasing form factor hardware is enabling computation and data storage to be embedded into many different devices. The combination of network connectivity and emerging distributed models of service orchestration is allowing the creation of new ways of measuring, monitoring and analysing performance. Using an approach inspired by the NIST seven layer model of cloud computing, we propose a model of connectivity that enables analytics services to be consumed across individual system components that are distributed, such as those found in the IoT and Industrial IoT (IIoT) domains.
Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval

Recently, deep supervised hashing methods have become popular for large-scale image retrieval task. To preserve the semantic similarity notion between examples, they typically utilize the pairwise supervision or the triplet supervised information for hash learning. However, these methods usually ignore the semantic class information which can help the improvement of the semantic discriminative ability of hash codes. In this paper, we propose a novel hierarchy neighborhood discriminative hashing method. Specifically, we construct a bipartite graph to build coarse semantic neighbourhood relationship between the sub-class feature centers and the embeddings features. Moreover, we utilize the pairwise supervised information to construct the fined semantic neighbourhood relationship between embeddings features. Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture. Experimental results on two largescale datasets demonstrate that the proposed method can outperform the state-of-the-art hashing methods.
New probes for axionlike particles at hadron colliders

Axion-like particles (ALPs) appear from spontaneous global symmetry breaking in many extensions of the Standard Model (SM). In this paper, we find bounds on ALP ($a$) model parameters at the LHC from the ALP production associated with a photon and a jet ($j+\gamma+a$) as well as single top and top quark pairs ($t+j+a$, $t\bar{t}+a$) in a model independent approach. In particular, it is shown that the ALP production associated with a photon plus a jet at the LHC is a promising channel with significant sensitivity to probe the ALP couplings to gluons and electroweak gauge bosons. The prospects are presented at the High Luminosity LHC including a realistic detector simulation and pile up effects. Furthermore, the ALP model is examined through its contributions to the top quark (chromo)magnetic dipole moments. It is shown that the top quark magnetic and chromomagnetic dipole moments enable us to probe the ALP couplings to top quark and gauge bosons at a time. The constraints are complementary to those obtained from direct searches, as they are sensitive to light ALPs.
Primitive rational points on expanding horocycles in products of the modular surface with the torus

We prove effective equidistribution of primitive rational points and of primitive rational points defined by monomials along long horocycle orbits in products of the torus and the modular surface. This answers a question posed in joint work by the first and the last named author with Shahar Mozes and Uri Shapira. Under certain congruence conditions we prove the joint equidistribution of conjugate rational points in the 2-torus and the modular surface.
Vertex algebras and Teichm\"{u}ller modular forms.

We associate to any holomorphic vertex algebra a collection of Teichm\"{u}ller modular forms, one in each genus. In genus one we obtain the character of the vertex algebra, and we thus reprove Zhu's modularity result. In higher genus, we prove that these forms have an expansion in terms of the correlation functions of the vertex algebra. We propose applications to the Schottky problem, to the study of the slope of the effective cone of the moduli space of curves, and to the classification of holomorphic vertex algebras. In particular, we prove a uniqueness result for high genera partition functions of the moonshine vertex algebra.
Fast GPU-Enabled Color Normalization for Digital Pathology

Normalizing unwanted color variations dne to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove these artifacts. Additionally, the original SPCN code is not readily usable on gigapixel whole slide images (WSIs) due to long run times, the use of proprietary software platform and libraries, and its inability to automatically handle WSIs. We completely rewrote the software such that it can automatically handle images of any size in popular WSI formats. Our software utilizes GPU-acceleration and open-source libraries that are becoming ubiquitous with the advent of deep learning. We also made several other small improvements and achieved a multifold overall speedup on gigapixel images, processing 109 pixels in 3 minutes. Our algorithm and software is usable right out-of-the-box by the computational pathology community.
An MBO scheme for clustering and semi-supervised clustering of signed networks

We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based diffuse interface model formulation utilizing the Ginzburg-Landau functional, based on an adaptation of the classic numerical Merriman-Bence-Osher (MBO) scheme for minimizing such graph-based functionals. The proposed objective function aims to minimize the total weight of inter-cluster positively-weighted edges, while maximizing the total weight of the inter-cluster negatively-weighted edges. Our method scales to large sparse networks, and can be easily adjusted to incorporate labelled data information, as is often the case in the context of semi-supervised learning. We tested our method on a number of both synthetic stochastic block models and real-world data sets (including financial correlation matrices), and obtained promising results that compare favourably against a number of state-of-the-art approaches from the recent literature.