Reviewerly/exHarmony huggingface dataset:
An analysis of optical contributions to a photo-sensor's ballistic fingerprints

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Lukáš, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a “ballistic” fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature. In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.
Adversarial Learning for Product Recommendation

Product recommendation can be considered as a problem in data fusion-- estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323 to 1.763 percent. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.
Trade-off relations of $$l_1$$-norm coherence for multipartite systems

We study the trade-off relations given by the l_1-norm coherence of general multipartite states. Explicit trade-off inequalities are derived with lower bounds given by the coherence of either bipartite or multipartite reduced density matrices. In particular, for pure three-qubit states, it is explicitly shown that the trade-off inequality is lower bounded by the three tangle of quantum entanglement.
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code.
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference

The neural attention mechanism plays an important role in many natural language processing applications.In particular, multi-head attention extends single-head attention by allowing a model to jointly attend information from different perspectives.However, without explicit constraining, multi-head attention may suffer from attention collapse, an issue that makes different heads extract similar attentive features, thus limiting the model's representation power.In this paper, for the first time, we provide a novel understanding of multi-head attention from a Bayesian perspective.Based on the recently developed particleoptimization sampling techniques, we propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model's expressiveness.Remarkably, our Bayesian interpretation provides theoretical inspirations on the not-well-understood questions: why and how one uses multi-head attention.Extensive experiments on various attention models and applications demonstrate that the proposed repulsive attention can improve the learned feature diversity, leading to more informative representations with consistent performance improvement on multiple tasks.
Nondestructive classification of quantum states using an algorithmic quantum computer

Methods of processing quantum data become more important as quantum computing devices improve their quality towards fault tolerant universal quantum computers. These methods include discrimination and filtering of quantum states given as an input to the device that may find numerous applications in quantum information technologies. In the present paper, we address a scheme of a classification of input states, which is nondestructive and deterministic for certain inputs, while probabilistic, in general case. This can be achieved by incorporating phase estimation algorithm into the hybrid quantum-classical computation scheme, where quantum block is trained classically. We perform proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience. Another aspect we are interested in is a mitigation of errors occurring due to the quantum device imperfections. We apply a series of heuristic tricks at the stage of classical postprocessing in order to improve raw experimental data and to recognize patterns in them. These ideas may find applications in other realization of hybrid quantum-classical computations with noisy quantum machines.
On the classical complexity of sampling from quantum interference of indistinguishable bosons

Experimental demonstration of the quantum advantage over classical simulations with Boson Sampling is currently under intensive investigation. There seems to be a scalability issue to the necessary number of bosons on the linear optical platforms and the experiments, such as the recent Boson Sampling with 20 photons on 60-port interferometer by H. Wang et al., Phys. Rev. Lett.123 (2019) 250503, are usually carried out on a small interferometer, much smaller than the size necessary for the no-collision regime. Before demonstration of quantum advantage, it is urgent to estimate exactly how the classical computations necessary for sampling from the output distribution of Boson Sampling are reduced when a smaller-size interferometer is used. This work supplies such a result, valid with arbitrarily close to 1 probability, which reduces in the no-collision regime to the previous estimate by Clifford and Clifford. One of the results with immediate application to current experiments with Boson Sampling is that classically sampling from the interference of [Formula: see text] single bosons on an [Formula: see text]-port interferometer is at least as hard as that with [Formula: see text] single bosons in the no-collision regime, i.e. on a much larger interferometer with at least [Formula: see text] ports.
AI-generated works and copyright law: towards a union of strange bedfellows

Copyright law vests in original works of authorship.1 However, most European Union (EU) copyright instruments do not clearly specify whether an ‘author’ is a natural and/or legal person.2 EU Member States have also adopted divergent interpretations of ‘authorship’. Though it appears that there might be a preference for the definition of an author as a natural person,3 this approach might not adequately reflect technological realities since copyright works are no longer the exclusive reserve of natural persons. This is particularly because AI systems are now (either autonomously or with human input) capable of creating works which would ordinarily have been eligible for copyright protection had they been created by natural persons. Owing to reasons which include the above, AI-generated works may not be subject to copyright protection in most EU countries and this might affect the incentivization of creatives.4 ...
Sideband transitions in a two-mode Josephson circuit driven beyond the rotating-wave approximation

Driving quantum systems periodically in time plays an essential role in the coherent control of quantum states. The rotating-wave approximation (RWA) is a good approximation technique for weak and nearly resonance driven fields. However, these experiments sometimes require large detuning and strong driving fields, for which the RWA may not hold. In this work, we experimentally, numerically, and analytically explore strongly driven two-mode Josephson circuits in the regime of strong driving and large detuning. Specifically, we investigate beam-splitter and two-mode squeezing interaction between the two modes induced by driving a two-photon sideband transition. Using numerical simulations, we observe that the RWA is unable to correctly capture the amplitude of the sideband transition rates. We verify this finding using an analytical model that is based on perturbative corrections. We find that the breakdown of the RWA in the regime studied does not lead to qualitatively different dynamics, but gives the same results as the RWA theory at higher drive strengths, enhancing the coupling rates compared to what one would predict. This is an interesting consequence compared to the carrier transition case, where the breakdown of the RWA results in qualitatively different time evolution of the quantum state. Our work provides an insight into the behavior of time-periodically driven systems beyond the RWA. We also provide a robust theoretical framework for including these findings in the calculation and calibration of quantum protocols in circuit quantum electrodynamics.
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection

Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to evaluate the matching quality between anchors and objects. In this paper, we question this use of IoU and propose a new anchor matching criterion guided, during the training phase, by the optimization of both the localization and the classification tasks: the predictions related to one task are used to dynamically assign sample anchors and improve the model on the other task, and vice versa. Despite the simplicity of the proposed method, our experiments with different state-of-the-art deep learning architectures on PASCAL VOC and MS COCO datasets demonstrate the effectiveness and generality of our Mutual Guidance strategy.
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning

Zero-shot learning (ZSL) makes object recognition in images possible in absence of visual training data for a part of the classes from a dataset. When the number of classes is large, classes are usually represented by semantic class prototypes learned automatically from unannotated text collections. This typically leads to much lower performances than with manually designed semantic prototypes such as attributes. While most ZSL works focus on the visual aspect and reuse standard semantic prototypes learned from generic text collections, we focus on the problem of semantic class prototype design for large scale ZSL. More specifically, we investigate the use of noisy textual metadata associated to photos as text collections, as we hypothesize they are likely to provide more plausible semantic embeddings for visual classes if exploited appropriately. We thus make use of a source-based filtering strategy to improve the robustness of semantic prototypes. Evaluation on the large scale ImageNet dataset shows a significant improvement in ZSL performances over two strong baselines, and over usual semantic embeddings used in previous works. We show that this improvement is obtained for several embedding methods, leading to state of the art results when one uses automatically created visual and text features.
Free-mode removal and mode decoupling for simulating general superconducting quantum circuits

Superconducting quantum circuits is one of the leading candidates for a universal quantum computer. Designing novel qubit and multiqubit superconducting circuits requires the ability to simulate and analyze the properties of a general circuit. In particular, going outside the transmon approach, we cannot make assumptions on anharmonicity, thus precluding blackbox quantization approaches and necessitating the formal circuit quantization approach. We consider and solve two issues involved in simulating general superconducting circuits. One of the issues is the handling of free modes in the circuit, that is, circuit modes with no potential term in the Hamiltonian. Another issue is circuit size, namely, the challenge of simulating strongly coupled multimode circuits. The main mathematical tool we use to address these issues is the linear canonical transformation in the setting of quantum mechanics. We address the first issue by giving a provably correct algorithm for removing free modes by performing a linear canonical transformation to completely decouple the free modes from other circuit modes. We address the second by giving a series of different linear canonical transformations to reduce intermode couplings, thereby reducing the problem to the weakly coupled case and greatly mitigating the overhead for classical simulation. We benchmark our decoupling methods by applying them to the circuit of two inductively coupled fluxonium qubits, obtaining several orders of magnitude reduction in the size of the Hilbert space that needs to be simulated.
Voter Verification of Ballot Marking Device Ballots Is a Two-Part Question: Can They? Mostly, They Can. Do They? Mostly, They Don't

The question of whether or not voters actually verify ballots produced by ballot marking devices (BMDs) is presently the subject of some controversy. Recent studies (e.g., Bernhard et al. 2020) suggest the verification rate is low. It is unclear if this is because voters cannot do this accurately or whether it is because voters simply choose not to attempt verification in the first place. In order to answer this question, we conducted an experiment in which 108 participants participated in a mock election where the BMD displayed the voters' true choices, but then changed a subset of those choices on the printed ballot. The design of the printed ballot, its length, the number and location of changes that were made to the ballot, and the instructions provided to the voters were manipulated as part of the experiment. Results indicated that of those voters who chose to examine the printed ballot, 76% detected anomalies, indicating that voters can reliably detect errors on their ballot if they simply review it. This suggests that administrative remedies, rather than attempts to alter fundamental human perceptual capabilities, could be employed to encourage voters to check their ballots, which could prove as an effective countermeasure.
A Machine Learning Method for Recognizing Invasive Content in Memes

In the time of web, Memes have become probably the sultriest subject on the web and apparently, the most widely recognized sort of satire seen via web-based networking media stages these days. Memes are visual outlines consolidated along with content which for the most part pass on amusing importance. Individuals use images to communicate via web-based networking media stage by posting them. Be that as it may, in spite of their enormous development, there isn’t a lot of consideration towards image wistful investigation. We will likely foresee the supposition covered up in the image by the joined investigation of the visual and literary traits. We propose a multimodal AI structure for estimation investigation of images. According to this, another Memes Sentiment Classification (MSC) strategy is anticipated which characterizes the memes-based pictures for offensive substance in a programmed way. This technique uses AI structure on the Image dataset and Python language model to gain proficiency with the visual and literary element of the image and consolidate them together to make forecasts. To do such a process, a few calculations have been utilized here like Logistic Regression (LR), and so forth. In the wake of looking at all these classifiers, LR outbursts with an accuracy of 72.48% over the PlantVillage dataset. In future degrees, the use of labels related to online networking posts which are treated as the mark of the post while gathering the information.
Many-body entanglement in fermion systems

We introduce a general bipartite-like representation and Schmidt decomposition of an arbitrary pure state of $N$ indistinguishable fermions, based on states of $M<N$ and $(N-M)$ fermions. It is directly connected with the reduced $M$- and $(N-M)$-body density matrices (DMs), which have the same spectrum in such states. The concept of $M$-body entanglement emerges naturally in this scenario, generalizing that of one-body entanglement. Rigorous majorization relations satisfied by the normalized $M$-body DM are then derived, which imply that the associated entropy will not increase, on average, under a class of operations which have these DMs as post-measurement states. Moreover, such entropy is an upper bound to the average bipartite entanglement entropy generated by a class of operations which map the original state to a bipartite state of $M$ and $N-M$ effectively distinguishable fermions. Analytic evaluation of the spectrum of $M$-body DMs in some strongly correlated fermionic states is also provided.
Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution

Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.
Graph-Based Knowledge Integration for Question Answering over Dialogue

Question answering over dialogue, a specialized machine reading comprehension task, aims to comprehend a dialogue and to answer specific questions.Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e.g., relationships between speakers).In this paper, we introduce a new approach for the task, featured by its novelty in structuring dialogue and integrating background knowledge for reasoning.Specifically, different from previous "structure-less" approaches, our method organizes a dialogue as a "relational graph", using edges to represent relationships between entities.To encode this relational graph, we devise a relational graph convolutional network (R-GCN), which can traverse the graph's topological structure and effectively encode multi-relational knowledge for reasoning.The extensive experiments have justified the effectiveness of our approach over competitive baselines.Moreover, a deeper analysis shows that our model is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences.
Low-latency perception in off-road dynamical low visibility environments

Robust systems are required for autonomous driving on non-uniform terrain commonly found in open-pit mines and developing countries. To help narrow the gap in this kind of application, this work proposes a perception system for autonomous vehicles and advanced driver assistance specialized on unpaved roads and off-road environments capable of navigating through rough terrain without a predefined trail. As part of this system, the Configurable Modular Segmentation Network (CMSNet) framework is proposed facilitating the creation of different architectures arrangements. Some CMSNet configurations were ported and trained to segment obstacles and trafficable ground on a new collection of images from unpaved roads and off-road scenarios containing adverse conditions such as night, rain, and dust. It was also performed: an investigation regarding the feasibility of applying deep learning to detect regions where the vehicle can pass through when there is no clear track boundary; a study of how our proposed segmentation algorithms behave in different severity levels of visibility impairment; and an evaluation of field tests carried out with semantic segmentation architectures conditioned for real-time inference. The new dataset (named Kamino) has almost 12,000 new images collected from an operated vehicle with various sensors, including eight cameras capturing synchronized sequences from different points of view. The Kamino dataset has a high number of labeled pixels compared to similar publicly available collections. It includes images collected from an off-road proving ground exclusively assembled for testing the system that emulates an open-pit mine scenario under different adverse conditions of visibility. To achieve embedded real-time inference and allows field tests, many layers of the CMSNet CNN networks were methodically removed and fused using TensorRT, C++, and CUDA. Empirical experiments on two datasets validated the effectiveness of the proposed system. The proposed solution achieves a mIoU of ∼87% (50.40% higher than the PSPNet and 56.21% than the DeepLabV3) on the Kamino dataset, and ∼81 % on the DepScene dataset, reaching up to 29 FPS on the RTX2060 GPU and almost 100 FPS with the optimized configurations, and low standard deviation values with the DrivePX2 computer platform.