Chandran et al. (SIAM J. Comput.14) formally introduced the cryptographic task of position verification, where they also showed that it cannot be achieved by classical protocols. In this work, we initiate the study of position verification protocols
with classical verifiers. We identify that proofs of quantumness (and thus computational assumptions) are necessary for such position verification protocols. For the other direction, we adapt the proof of quantumness protocol by Brakerski et al. (FOCS18) to instantiate such a position verification protocol. As a result, we achieve classically verifiable position verification assuming the quantum hardness of Learning with Errors. Along the way, we develop the notion of 1-of-2 non-local soundness for the framework of 1-of-2 puzzles, first introduced by Radian and Sattath (AFT19), which can be viewed as a computational unclonability property. We show that 1-of-2 non-local soundness follows from the standard 2-of-2 soundness, which could be of independent interest.
Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated data
sets, which are expensive to obtain in practice. In this work, we explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible for retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both networks are initialized respectively with pseudo data built from an unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to the previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.
Although general relativity (GR) has been precisely tested at the solar system scale, precise tests at a galactic or cosmological scale are still relatively insufficient. Here, in order to test GR at the galactic scale, we use the newly compiled gala
xy-scale strong gravitational lensing (SGL) sample to constrain the parameter $gamma_{PPN}$ in the parametrized post-Newtonian (PPN) formalism. We employ the Pantheon sample of type Ia supernovae observation to calibrate the distances in the SGL systems using the Gaussian Process method, which avoids the logical problem caused by assuming a cosmological model within GR to determine the distances in the SGL sample. Furthermore, we consider three typical lens models in this work to investigate the influences of the lens mass distributions on the fitting results. We find that the choice of the lens models has a significant impact on the constraints on the PPN parameter $gamma_{PPN}$. We use the Bayesian information criterion as an evaluation tool to make a comparison for the fitting results of the three lens models, and we find that the most reliable lens model gives the result of $gamma_{PPN}=1.065^{+0.064}_{-0.074}$, which is in good agreement with the prediction of $gamma_{PPN}=1$ by GR. As far as we know, our 6.4% constraint result is the best result so far among the recent works using the SGL method.
In this work, we present a new multi-view depth estimation method that utilizes both conventional SfM reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF). Unlike existing neural network based optimization
method that relies on estimated correspondences, our method directly optimizes over implicit volumes, eliminating the challenging step of matching pixels in indoor scenes. The key to our approach is to utilize the learning-based priors to guide the optimization process of NeRF. Our system firstly adapts a monocular depth network over the target scene by finetuning on its sparse SfM reconstruction. Then, we show that the shape-radiance ambiguity of NeRF still exists in indoor environments and propose to address the issue by employing the adapted depth priors to monitor the sampling process of volume rendering. Finally, a per-pixel confidence map acquired by error computation on the rendered image can be used to further improve the depth quality. Experiments show that our proposed framework significantly outperforms state-of-the-art methods on indoor scenes, with surprising findings presented on the effectiveness of correspondence-based optimization and NeRF-based optimization over the adapted depth priors. In addition, we show that the guided optimization scheme does not sacrifice the original synthesis capability of neural radiance fields, improving the rendering quality on both seen and novel views. Code is available at https://github.com/weiyithu/NerfingMVS.
In this paper, we show the following: the Hausdorff dimension of the spectrum of period-doubling Hamiltonian is bigger than $log alpha/log 4$, where $alpha$ is the Golden number; there exists a dense uncountable subset of the spectrum such that for e
ach energy in this set, the related trace orbit is unbounded, which is in contrast with a recent result of Carvalho (Nonlinearity 33, 2020); we give a complete characterization for the structure of gaps and the gap labelling of the spectrum. All of these results are consequences of an intrinsic coding of the spectrum we construct in this paper.
In this paper, $C^{0}$ finite determination of $Gamma-$equivariant bifurcation problems in the relative case from the weighted point view is being discussed . Some criteria on the $C^{0}$ finite determination of $Gamma-$equi-variant bifurcation probl
ems in the relative case are then obtained in terms of an analytic-geometric non-degeneracy condition, which generalize the result on the $C^{0}$ finite determination of bifurcation problems given by P.B.Percell and P.N.Brown.
This article proposes a novel method for unbiased PDF updating by using the forward-backward asymmetry $(A_{FB})$ in the Drell-Yan $pp rightarrow Z/gamma^{*} rightarrow ell^+ell^-$ process. The $A_{FB}$ spectrum, as a function of the dilepton mass, i
s not only governed by the electroweak (EW) fundamental parameter, i.e. the weak mixing angle $sin^2 theta_{W}$, but also sensitive to the parton distribution functions (PDFs). When performing simultaneous or iterative fittings for the PDF updating and EW parameter extraction with the same $A_{FB}$, the strong correlations between them may induce large bias into these two sectors. From our studies, it was found that the sensitivity of $A_{FB}$ on $sin^2 theta_{W}$ is dominated by its average value around the $Z$ pole region, while the shape (or gradient) of the $A_{FB}$ spectrum is insensitive to $sin^2 theta_{W}$ but highly sensitive to the PDF modeling. Accordingly, a new observable related to the gradient of the spectrum is defined, and demonstrated to have the capability of significantly reducing the correlation and potential bias between the PDF updating and electroweak measurement. Moreover, the well-defined observable will provide unique information on the sea-valence PDF ratios of the first generation quarks.
The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional
network captures the topological graph feature. However, the existing works (i) lack a flexible combination mechanism to adaptively fuse those two kinds of features for learning the discriminative representation and (ii) overlook the multi-scale information embedded at different layers for subsequent cluster assignment, leading to inferior clustering results. To this end, we propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN). Specifically, AGCN exploits a heterogeneity-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. Moreover, AGCN develops a scale-wise fusion module to adaptively aggregate the multi-scale features embedded at different layers. Based on a unified optimization framework, AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion. Compared with the existing deep clustering methods, our method is more flexible and effective since it comprehensively considers the numerous and discriminative information embedded in the network and directly produces the clustering results. Extensive quantitative and qualitative results on commonly used benchmark datasets validate that our AGCN consistently outperforms state-of-the-art methods.
Fault localization is to identify faulty source code. It could be done on various granularities, e.g., classes, methods, and statements. Most of the automated fault localization (AFL) approaches are coarse-grained because it is challenging to accurat
ely locate fine-grained faulty software elements, e.g., statements. SBFL, based on dynamic execution of test cases only, is simple, intuitive, and generic (working on various granularities). However, its accuracy deserves significant improvement. To this end, in this paper, we propose a hybrid fine-grained AFL approach based on both dynamic spectrums and static statement types. The rationale of the approach is that some types of statements are significantly more/less error-prone than others, and thus statement types could be exploited for fault localization. On a crop of faulty programs, we compute the error-proneness for each type of statements, and assign priorities to special statement types that are steadily more/less error-prone than others. For a given faulty program under test, we first leverage traditional spectrum-based fault localization algorithm to identify all suspicious statements and to compute their suspicious scores. For each of the resulting suspicious statements, we retrieve its statement type as well as the special priority associated with the type. The final suspicious score is the product of the SBFL suspicious score and the priority assigned to the statement type. A significant advantage of the approach is that it is simple and intuitive, making it efficient and easy to interpret/implement. We evaluate the proposed approach on widely used benchmark Defects4J. The evaluation results suggest that the proposed approach outperforms widely used SBFL, reducing the absolute waste effort (AWE) by 9.3% on average.
Deep neural networks (DNNs) are under threat from adversarial example attacks. The adversary can easily change the outputs of DNNs by adding small well-designed perturbations to inputs. Adversarial example detection is a fundamental work for robust D
NNs-based service. Adversarial examples show the difference between humans and DNNs in image recognition. From a human-centric perspective, image features could be divided into dominant features that are comprehensible to humans, and recessive features that are incomprehensible to humans, yet are exploited by DNNs. In this paper, we reveal that imperceptible adversarial examples are the product of recessive features misleading neural networks, and an adversarial attack is essentially a kind of method to enrich these recessive features in the image. The imperceptibility of the adversarial examples indicates that the perturbations enrich recessive features, yet hardly affect dominant features. Therefore, adversarial examples are sensitive to filtering off recessive features, while benign examples are immune to such operation. Inspired by this idea, we propose a label-only adversarial detection approach that is referred to as feature-filter. Feature-filter utilizes discrete cosine transform to approximately separate recessive features from dominant features, and gets a mutant image that is filtered off recessive features. By only comparing DNNs prediction labels on the input and its mutant, feature-filter can real-time detect imperceptible adversarial examples at high accuracy and few false positives.
