Reviewerly/exHarmony huggingface dataset:
Semantics-Preserving Graph Propagation for Zero-Shot Object Detection

Most existing object detection models are restricted to detecting objects from previously seen categories, an approach that tends to become infeasible for rare or novel concepts. Accordingly, in this paper, we explore object detection in the context of zero-shot learning, i.e., Zero-Shot Object Detection (ZSD), to concurrently recognize and localize objects from novel concepts. Existing ZSD algorithms are typically based on a simple mapping-transfer strategy that is susceptible to the domain shift problem. To resolve this problem, we propose a novel Semantics-Preserving Graph Propagation model for ZSD based on Graph Convolutional Networks (GCN). More specifically, we employ a graph construction module to flexibly build category graphs by incorporating diverse correlations between category nodes; this is followed by two semantics preserving modules that enhance both category and region representations through a multi-step graph propagation process. Compared to existing mapping-transfer based methods, both the semantic description and semantic structural knowledge exhibited in prior category graphs can be effectively leveraged to boost the generalization capability of the learned projection function via knowledge transfer, thereby providing a solution to the domain shift problem. Experiments on existing seen/unseen splits of three popular object detection datasets demonstrate that the proposed approach performs favorably against state-of-the-art ZSD methods.
Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks

The authors train a Siamese neural network to decide whether two different descriptions describe the same physical object. The neural network learns to identify the objects by calculating the underlying invariants and conserved quantities.
Domain wall encoding of discrete variables for quantum annealing and QAOA

Abstract In this paper I propose a new method of encoding discrete variables into Ising model qubits for quantum optimisation. The new method is based on the physics of domain walls in one-dimensional Ising spin chains. I find that these encodings and the encoding of arbitrary two variable interactions is possible with only two body Ising terms Following on from similar results for the ‘one hot’ method of encoding discrete variables (Hadfield et al 2019 Algorithms 12 34) I also demonstrate that it is possible to construct two body mixer terms which do not leave the logical subspace, an important consideration for optimising using the quantum alternating operator ansatz. I additionally discuss how, since the couplings in the domain wall encoding only need to be ferromagnetic and therefore could in principle be much stronger than anti-ferromagnetic couplers, application specific quantum annealers for discrete problems based on this construction may be beneficial. Finally, I compare embedding for synthetic scheduling and colouring problems with the domain wall and one hot encodings on two graphs which are relevant for quantum annealing, the chimera graph and the Pegasus graph. For every case I examine I find a similar or better performance from the domain wall encoding as compared to one hot, but this advantage is highly dependent on the structure of the problem. For encoding some problems, I find an advantage similar to the one found by embedding in a Pegasus graph compared to embedding in a chimera graph.
A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification

Being one of the most widely used social media tools, Twitter is seen as an important source of information for acquiring people's attitudes, emotions, views and feedbacks.Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion.In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis.Although CNN can extract high-level local features efficiently by using convolutional layer and max-pooling layer, it cannot effectively learn sequence of correlations.On the other hand, Bi-LSTM uses two LSTM directions to improve the contexts available to deep learning algorithms, but Bi-LSTM cannot extract local features in a parallel way.Therefore, applying a single CNN or single Bi-LSTM for sentiment analysis cannot achieve the optimal classification result.An integrating structure of CNN and Bi-LSTM model is proposed in this study.ConvBiLSTM is implemented; a word embedding model which converts tweets into numerical values, CNN layer receives feature embedding as input and produces smaller dimension of features, and the Bi-LSTM model takes the input from the CNN layer and produces classification result.Word2Vec and GloVe were distinctly applied to observe the impact of the word embedding result on the proposed model.ConvBiLSTM was applied with retrieved Tweets and SST-2 datasets.ConvBiLSTM model with Word2Vec on retrieved Tweets dataset outperformed the other models with 91.13% accuracy.
Quantum Internet protocol stack: A comprehensive survey

Classical Internet evolved exceptionally during the last five decades, from a network comprising a few static nodes in the early days to a leviathan interconnecting billions of devices. This has been possible by the separation of concern principle, for which the network functionalities are organized as a stack of layers, each providing some communication functionalities through specific network protocols. In this survey, we aim at highlighting the impossibility of adapting the classical Internet protocol stack to the Quantum Internet, due to the marvels of quantum mechanics. Indeed, the design of the Quantum Internet requires a major paradigm shift of the whole protocol stack for harnessing the peculiarities of quantum entanglement and quantum information. In this context, we first overview the relevant literature about Quantum Internet protocol stack. Then, stemming from this, we sheds the light on the open problems and required efforts toward the design of an effective and complete Quantum Internet protocol stack. To the best of authors’ knowledge, a survey of this type is the first of its own. What emerges from this analysis is that the Quantum Internet, though still in its infancy, is a disruptive technology whose design requires an inter-disciplinary effort at the border between quantum physics, computer and telecommunications engineering.
Selection Methods for Evolutionary Algorithms

Selection pressure can have a decisive effect on the outcome of an evolutionary search. Try too hard, and you will end up converging prematurely, perhaps on a local maximum, perhaps not even that. Conversely, too little selection pressure, apart from wasting time, may allow the effects of genetic drift to dominate, again leading to a suboptimal result. In nature, there are two aspects to breeding success: surviving long enough to reach reproductive maturity, and then persuading a mate to be your partner. In simulations, such subtleties are mostly the province of artificial life experiments where, for example, an animal that fails to find enough food may die. In such systems it is possible for the whole population to die out, which may be realistic but does rather terminate the search. In most Evolutionary Algorithms (EA), therefore, a more interventionist approach is taken, with reproductive opportunities being allocated on the basis of relative fitness. There are a variety of selection strategies in common use, not all of which use the fitness values directly. Some order the population, and allocate trials by rank, others conduct tournaments, giving something of the flavour of the natural competition for mates. Each of the schools of EA has its own methods of selection, though GA practitioners in particular have experimented with several algorithms. The aim of this chapter is to explain the differences between them, and give some indication of their relative merits.
A CNN-based face detector with a simple feature map and a coarse-to-fine classifier - Withdrawn

Withdrawn.
Transformation-Based Streaming Workflow Allocation on Geo-Distributed Datacenters for Streaming Big Data Processing

The cost-minimization problem for streaming workflow (SW) has already become increasingly important and even critical in stream big data processing, particularly for geographically distributed datacenters, because of its huge demand on computing and communicating resources. Existing virtual machine (VM) allocation algorithms in cloud computing have been widely applied to batch-processing models; however, none of them can be successfully applied to SW because: 1) they failed to adapt the continuous execution characteristic of SW; and 2) most of them are all based on the assumption that the price of traffic and VMs among datacenters are uniform. In this paper, we propose a transformation-based SW allocation algorithm with the goal of cost-minimization for stream big data processing in geographically distributed datacenters, considering the characteristics of SW and price heterogeneity among geographically distributed datacenters. We first propose a cost-aware workflow transformation framework based on eight well-designed and verified transformation rules for cost reduction to adapt the continuous execution characteristic of SW. We then formulate the joint VM-traffic optimization problem and show that it is NP-hard. To produce the optimal solution in polynomial time, we then transform the SW allocation problem into the minimum-cost maximum-flow problem, considering both traffic and VMs price heterogeneity. Finally, our experimental results validate the high cost efficiency of our approach with lower computing and communicating costs by optimizing the workflow specification and joint VM-traffic cost optimization.
KI-Mon ARM: A Hardware-Assisted Event-triggered Monitoring Platform for Mutable Kernel Object

External hardware-based kernel integrity monitors have been proposed to mitigate kernel-level malwares. However, the existing external approaches have been limited to monitoring the static regions of kernel while the latest rootkits manipulate the dynamic kernel objects. To address the issue, we present KI-Mon, a hardware-based platform that introduces event-triggered monitoring techniques for kernel dynamic objects. KI-Mon advances the bus traffic snooping technique to not only detect memory write traffic on the host bus but also filter out all but meaningful traffic to generate events. We show how kernel invariant verification software can be developed around these events, and also provide a set of APIs for additional invariant verification development. We also report our findings and considerations on the unique challenges for external monitors – such as cache coherency, dynamic object tracing. We introduce host-side kernel changes that alleviate these issues that involve changes in kernel's object allocation and cache policy control. We have built a prototype of KI-Mon on the ARM architecture to demonstrate the efficacy of KI-Mon's event-triggered mechanism in terms of performance overhead for the monitored host system and the processor usage of the KI-Mon processor.
Minimal and canonical images

We describe a family of new algorithms for finding the canonical image of a set of points under the action of a permutation group. This family of algorithms makes use of the orbit structure of the group, and a chain of subgroups of the group, to efficiently reduce the amount of search that must be performed to find a canonical image. We present a formal proof of correctness of our algorithms and describe experiments on different permutation groups that compare our algorithms with the previous state of the art.
Fast multi-output relevance vector regression

This paper has applied the matrix Gaussian distribution of the likelihood function of the complete data set to reduce time complexity of multi-output relevance vector regression from OVM3 to OV3+M3, where V and M are the number of output dimensions and basis functions respectively and V < M. Our experimental results demonstrate that the proposed method is more competitive and faster than the existing methods like Thayananthan et al. (2008). Its computational efficiency and accuracy can be attributed to the different model specifications of the likelihood of the data, as the existing method expresses the likelihood of the training data as the product of Gaussian distributions whereas the proposed method expresses it as the matrix Gaussian distribution.
Epistemic graphs for representing and reasoning with positive and negative influences of arguments

This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine–grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context–sensitive. It also allows for better modelling of imperfect agents, which can be important in multi–agent applications.
Measurement Errors in R

This paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the 'errors' package, which provides a class for associating uncertainty metadata, automated propagation and reporting. Working with 'errors' enables transparent, lightweight, less error-prone handling and convenient representation of measurements with errors. Finally, we discuss the advantages, limitations and future work of computing with errors.
Fault-tolerant quantum metrology

We introduce the notion of fault-tolerant quantum metrology to overcome noise beyond our control, associated with sensing the parameter, by reducing the noise in operations under our control, associated with preparing and measuring probes and ancillae. To that end, we introduce noise thresholds to quantify the noise resilience of parameter estimation schemes. We demonstrate improved noise thresholds over the non-fault-tolerant schemes. We use quantum Reed-Muller codes to retrieve more information about a single phase parameter being estimated in the presence of full-rank Pauli noise. Using only error detection, as opposed to error correction, allows us to retrieve higher thresholds. We show that better devices, which can be engineered, can enable us to counter larger noise in the field beyond our control. Further improvements in fault-tolerant quantum metrology could be achieved by optimizing in tandem parameter-specific estimation schemes and transversal quantum error correcting codes.
Inequivalent multipartite coherence classes and two operational coherence monotones

Quantum coherence has received significant attention in recent years, but the structure of multipartite coherent states is unclear. In this paper, we generalize important results in multipartite entanglement theory to their counterparts in quantum coherence theory. First, we give a necessary and sufficient condition for when two pure multipartite states are equivalent under local incoherent operations assisted by classical communications (LICC), i.e., two states can be deterministically transformed to each other under LICC operations. Next, we investigate and give the conditions in which such a transformation succeeds only stochastically. Different from the entanglement case for two-qubit states, we find that the stochastic LICC (sLICC) equivalence classes are infinite. Moreover, it is possible that there are some classes of states in multipartite entanglement that can convert into each other, whereas they cannot convert into each other in multipartite coherence. In order to show the difference among sLICC classes, we introduce two coherence monotones: accessible coherence and source coherence, following the logistics given in [Phys. Rev. Lett. 115, 150502 (2015)]. These coherence monotones have a straightforward operational interpretation, namely, the accessible coherence characterizes the proficiency of a state to generate other states via quantum incoherent operations, whereas the source coherence characterizes the set of states that can be reached via quantum incoherent operations acting on the given state of interest.
Universal detection of entanglement in two-qubit states using only two copies

We revisit the problem of detection of entanglement of an unknown two-qubit state using minimal resources. Using weak values and just two copies of an arbitrary two-qubit state, we present a protocol where a post selection measurement in the computational basis provides enough information to identify if the state is entangled or not. Our protocol enables complete state identification with a single-setting post selection measurement on two copies of the state. It follows that by restricting to pure states, the global interaction required for determining the weak values can be realized by local operations. We further show that our protocol is robust against errors arising from inappropriate global interactions applied during weak value determination.
Interactive obstruction-free lensing for volumetric data visualization

Occlusion is an issue in volumetric visualization as it prevents direct visualization of the region of interest. While many techniques such as transfer functions, volume segmentation or view distortion have been developed to address this, there is still room for improvement to better support the understanding of objects' vicinity. However, most existing Focus+Context fail to solve partial occlusion in datasets where the target and the occluder are very similar density-wise. For these reasons, we investigate a new technique which maintains the general structure of the investigated volumetric dataset while addressing occlusion issues. With our technique, the user interactively defines an area of interest where an occluded region or object is partially visible. Then our lens starts pushing at its border occluding objects, thus revealing hidden volumetric data. Next, the lens is modified with an extended field of view (fish-eye deformation) to better see the vicinity of the selected region. Finally, the user can freely explore the surroundings of the area under investigation within the lens. To provide real-time exploration, we implemented our lens using a GPU accelerated ray-casting framework to handle ray deformations, local lighting, and local viewpoint manipulation. We illustrate our technique with five application scenarios in baggage inspection, 3D fluid flow visualization, chest radiology, air traffic planning, and DTI fiber exploration.
Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

In this paper, we address the registration of historical WWII images to present-day ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough Voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps.
Polarization Oscillations in Birefringent Emitter-Cavity Systems

We present the effects of resonator birefringence on the cavity-enhanced interfacing of quantum states of light and matter, including the first observation of single photons with a time-dependent polarization state that evolves within their coherence time. A theoretical model is introduced and experimentally verified by the modified polarization of temporally long single photons emitted from a ^{87}Rb atom coupled to a high-finesse optical cavity by a vacuum-stimulated Raman adiabatic passage process. Further theoretical investigation shows how a change in cavity birefringence can both impact the atom-cavity coupling and engender starkly different polarization behavior in the emitted photons. With polarization a key resource for encoding quantum states of light and modern micron-scale cavities particularly prone to birefringence, the consideration of these effects is vital to the faithful realization of efficient and coherent emitter-photon interfaces for distributed quantum networking and communications.
A Perceptual Distinguishability Predictor For JND-Noise-Contaminated Images

Just noticeable difference (JND) models are widely used for perceptual redundancy estimation in images and videos. A common method for measuring the accuracy of a JND model is to inject random noise in an image based on the JND model, and check whether the JND-noise-contaminated image is perceptually distinguishable from the original image or not. Also, when comparing the accuracy of two different JND models, the model that produces the JND-noise-contaminated image with better quality at the same level of noise energy is the better model. But in both of these cases, a subjective test is necessary, which is very time consuming and costly. In this paper, we present a full-reference metric called PDP (perceptual distinguishability predictor), which can be used to determine whether a given JND-noise-contaminated image is perceptually distinguishable from the reference image. The proposed metric employs the concept of sparse coding, and extracts a feature vector out of a given image pair. The feature vector is then fed to a multilayer neural network for classification. To train the network, we built a public database of 999 natural images with distinguishbility thresholds for four different JND models obtained from an extensive subjective experiment. The results indicated that PDD achieves high classification accuracy of 97.1%. The proposed method can be used to objectively compare various JND models without performing any subjective test. It can also be used to obtain proper scaling factors to improve the JND thresholds estimated by an arbitrary JND model.