Reviewerly/exHarmony huggingface dataset:
Quartz: superoptimization of Quantum circuits

Existing quantum compilers optimize quantum circuits by applying circuit transformations designed by experts. This approach requires significant manual effort to design and implement circuit transformations for different quantum devices, which use different gate sets, and can miss optimizations that are hard to find manually. We propose Quartz, a quantum circuit superoptimizer that automatically generates and verifies circuit transformations for arbitrary quantum gate sets. For a given gate set, Quartz generates candidate circuit transformations by systematically exploring small circuits and verifies the discovered transformations using an automated theorem prover. To optimize a quantum circuit, Quartz uses a cost-based backtracking search that applies the verified transformations to the circuit. Our evaluation on three popular gate sets shows that Quartz can effectively generate and verify transformations for different gate sets. The generated transformations cover manually designed transformations used by existing optimizers and also include new transformations. Quartz is therefore able to optimize a broad range of circuits for diverse gate sets, outperforming or matching the performance of hand-tuned circuit optimizers.
3D Object Detection Based on Attention and Multi-Scale Feature Fusion

Three-dimensional object detection in the point cloud can provide more accurate object data for autonomous driving. In this paper, we propose a method named MA-MFFC that uses an attention mechanism and a multi-scale feature fusion network with ConvNeXt module to improve the accuracy of object detection. The multi-attention (MA) module contains point-channel attention and voxel attention, which are used in voxelization and 3D backbone. By considering the point-wise and channel-wise, the attention mechanism enhances the information of key points in voxels, suppresses background point clouds in voxelization, and improves the robustness of the network. The voxel attention module is used in the 3D backbone to obtain more robust and discriminative voxel features. The MFFC module contains the multi-scale feature fusion network and the ConvNeXt module; the multi-scale feature fusion network can extract rich feature information and improve the detection accuracy, and the convolutional layer is replaced with the ConvNeXt module to enhance the feature extraction capability of the network. The experimental results show that the average accuracy is 64.60% for pedestrians and 80.92% for cyclists on the KITTI dataset, which is 1.33% and 2.1% higher, respectively, compared with the baseline network, enabling more accurate detection and localization of more difficult objects.
Using Augmented Reality in K-12 Education: An Indicative Platform for Teaching Physics

Augmented Reality (AR) could provide key benefits in education and create a richer user experience by increasing the motivation and engagement of the students. To this end, the current paper presents a system with three AR applications for teaching physics in the fifth and sixth grades of primary school and in the first grade of secondary school, and the ultimate goal is the development of a unified platform that covers the subject of physics in all classes of K-12 education. The platform provides a useful tool to familiarize both teachers and pupils with AR technologies, aiming to improve the learning and teaching experience and to enhance their skills. The developed system is evaluated in terms of usability, gamification and willingness of the teachers to incorporate this technology into the teaching process. A total of 314 users participated in the research, where they were divided into three user groups: (i) teachers (N = 15), (ii) pupils (N = 189) and (iii) computer science students (N = 110). The outcomes were satisfactory, revealing that the gamified AR applications are easy to use, and teachers are interested in using these AR applications in their classrooms.
Optimal Broadcast Encryption and CP-ABE from Evasive Lattice Assumptions

We present a new, simple candidate broadcast encryption scheme for N users with parameter size $$\textsf {poly}(\log N)$$ . We prove security of our scheme under a non-standard variant of the LWE assumption where the distinguisher additionally receives short Gaussian pre-images while avoiding zeroizing attacks. This yields the first candidate optimal broadcast encryption that is plausibly post-quantum secure, and enjoys a security reduction to a simple assumption. As a secondary contribution, we present a candidate ciphertext-policy attribute-based encryption (CP-ABE) scheme for circuits of a-priori bounded polynomial depth where the parameter size is independent of the circuit size, and prove security under an additional non-standard assumption.
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers

Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings.As most research on active learning has been carried out before transformer-based language models ("transformers") became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date.This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings.For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers.In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks.For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification.
Rethinking the Reference-based Distinctive Image Captioning

Distinctive Image Captioning (DIC) --- generating distinctive captions that describe the unique details of a target image --- has received considerable attention over the last few years. A recent DIC work proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i.e., reference-based DIC (Ref-DIC). It aims to make the generated captions can tell apart the target and reference images. Unfortunately, reference images used by existing Ref-DIC works are easy to distinguish: these reference images only resemble the target image at scene-level and have few common objects, such that a Ref-DIC model can trivially generate distinctive captions even without considering the reference images. For example, if the target image contains objects "towel'' and "toilet'' while all reference images are without them, then a simple caption "A bathroom with a towel and a toilet'' is distinctive enough to tell apart target and reference images. To ensure Ref-DIC models really perceive the unique objects (or attributes) in target images, we first propose two new Ref-DIC benchmarks. Specifically, we design a two-stage matching mechanism, which strictly controls the similarity between the target and reference images at object-/attribute- level (vs. scene-level). Secondly, to generate distinctive captions, we develop a strong Transformer-based Ref-DIC baseline, dubbed as TransDIC. It not only extracts visual features from the target image, but also encodes the differences between objects in the target and reference images. Finally, for more trustworthy benchmarking, we propose a new evaluation metric named DisCIDEr for Ref-DIC, which evaluates both the accuracy and distinctiveness of the generated captions. Experimental results demonstrate that our TransDIC can generate distinctive captions. Besides, it outperforms several state-of-the-art models on the two new benchmarks over different metrics.
A vision-based deep learning approach for independent-users Arabic sign language interpretation

Abstract More than 5% of the people around the world are deaf and have severe difficulties in communicating with normal people according to the World Health Organization (WHO). They face a real challenge to express anything without an interpreter for their signs. Nowadays, there are a lot of studies related to Sign Language Recognition (SLR) that aims to reduce this gap between deaf and normal people as it can replace the need for an interpreter. However, there are a lot of challenges facing the sign recognition systems such as low accuracy, complicated gestures, high-level noise, and the ability to operate under variant circumstances with the ability to generalize or to be locked to such limitations. Hence, many researchers proposed different solutions to overcome these problems. Each language has its signs and it can be very challenging to cover all the languages’ signs. The current study objectives: (i) presenting a dataset of 20 Arabic words, and (ii) proposing a deep learning (DL) architecture by combining convolutional neural network (CNN) and recurrent neural network (RNN). The suggested architecture reported 98% accuracy on the presented dataset. It also reported 93.4% and 98.8% for the top-1 and top-5 accuracies on the UCF-101 dataset.
Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities

Deployment planning of distributed rooftop photovoltaic (PV) systems remains a critical challenge for high-density cities, due to complex shading effects and diversified rooftop availabilities. Furthermore, such planning for large-scale systems could be extremely complex due to high dimensionality caused by the enormous number of buildings. To tackle the challenge, this study proposed an optimal planning strategy for municipal-scale distributed rooftop PV systems in high-density cities. The optimization problem was solved by integer learning programming, based on high-accuracy solar energy potentials characterization. By selecting proper rooftops for PV, the electricity generation was maximized, considering the conflicting budget and peak-export-power constraints. A Hong Kong-based case study (including 582 real building rooftops) was conducted. The effectiveness of the proposed strategy was verified by comparing with 5,000,000 Monte-Carlo-generated alternatives. The strategy more effectively identified the proper rooftops for PV installations, achieving up to 17.7% improvements in performance-cost ratio. Furthermore, the optimal planning strategy was systematically compared with two heuristic planning methods, i.e., total-energy-prioritized and energy-intensity-prioritized methods. The strategy outperformed the heuristic methods by up to 23.3% through well considering trade-off between rooftop total energy and energy intensity. The developed strategy can be used to facilitate rooftop PV deployments, and thus contribute to urban decarbonization.
Deformable Video Transformer

Video transformers have recently emerged as an effective alternative to convolutional networks for action classification. However, most prior video transformers adopt either global space-time attention or hand-defined strategies to compare patches within and across frames. These fixed attention schemes not only have high computational cost but, by comparing patches at predetermined locations, they neglect the motion dynamics in the video. In this paper, we introduce the Deformable Video Transformer (DVT), which dynamically predicts a small subset of video patches to attend for each query location based on motion information, thus allowing the model to decide where to look in the video based on correspondences across frames. Crucially, these motion-based correspondences are obtained at zero-cost from information stored in the compressed format of the video. Our deformable attention mechanism is optimized directly with respect to classification performance, thus eliminating the need for suboptimal hand-design of attention strategies. Experiments on four large-scale video benchmarks (Kinetics-400, Something-Something-V2, EPIC-KITCHENS and Diving-48) demonstrate that, compared to existing video transformers, our model achieves higher accuracy at the same or lower computational cost, and it attains state-of-the-art results on these four datasets.
MuIT: An End-to-End Multitask Learning Transformer

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://ivrl.github.io/MulT/.
Region-Aware Face Swapping

This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: 1) Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned crossscale semantic interaction. 2) Global Source Feature-Adaptive (SFA) branch further complements global identity-relevant cues for generating identity-consistent swapped faces. Besides, we propose a Face Mask Predictor (FMP) module incorporated with StyleGAN2 to predict identity-relevant soft facial masks in an unsupervised manner that is more practical for generating harmonious high-resolution faces. Abundant experiments qualitatively and quantitatively demonstrate the superiority of our method for generating more identity-consistent high-resolution swapped faces over SOTA methods, e.g., obtaining 96.70 ID retrieval that outperforms SOTA MegaFS by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.87\uparrow$</tex> .
Time complexity analysis of quantum algorithms via linear representations for nonlinear ordinary and partial differential equations

We construct quantum algorithms to compute the solution and/or physical observables of nonlinear ordinary differential equations (ODEs) and nonlinear Hamilton-Jacobi equations (HJE) via linear representations or exact mappings between nonlinear ODEs/HJE and linear partial differential equations (the Liouville equation and the Koopman-von Neumann equation). The connection between the linear representations and the original nonlinear system is established through the Dirac delta function or the level set mechanism. We compare the quantum linear systems algorithms based methods and the quantum simulation methods arising from different numerical approximations, including the finite difference discretisations and the Fourier spectral discretisations for the two different linear representations, with the result showing that the quantum simulation methods usually give the best performance in time complexity. We also propose the Schrödinger framework to solve the Liouville equation for the HJE with the Hamiltonian formulation of classical mechanics, since it can be recast as the semiclassical limit of the Wigner transform of the Schrödinger equation. Comparison between the Schrödinger and the Liouville framework will also be made.
Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS

This paper presents findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In this paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for ad-hoc search based tasks at Video Browser Showdown is introduced.
Conceptual Issues in the Development of Spatial Cognition

This chapter considers the value of approaching the study of spatial cognition from a developmental perspective. It examines several classic studies in the literature on spatial cognitive development to illustrate the ways in which inappropriate methodologies have led to unjustified conclusions. The chapter argues that investigators often pay insufficient attention to the selection of tasks, dependent measures, and analytic techniques. The definitional and methodological issues are relevant for both developmental and non-developmental approaches to spatial cognition. The contribution of a developmental approach to the study of spatial cognition, however, extends beyond its role in highlighting definitional and methodological troublespots in empirical work. Investigators studying the development of spatial cognition—like the blind men approaching the elephant—are all groping at isolated parts. The chapter concludes with a brief discussion of the necessity for more careful establishment of baseline performance on a variety of spatial tasks.
Non-linear Optimization

Many machine learning methods require non-linear optimization, performed by the backward propagation of model errors, with the process complicated by the presence of multiple minima and saddle points. Numerous gradient descent algorithms are available for optimization, including stochastic gradient descent, conjugate gradient, quasi-Newton and non-linear least squares such as Levenberg-Marquardt. In contrast to deterministic optimization, stochastic optimization methods repeatedly introduce randomness during the search process to avoid getting trapped in a local minimum. Evolutionary algorithms, borrowing concepts from evolution to solve optimization problems, include genetic algorithm and differential evolution.
Taking Time to Structure Discourse: Pronoun Generation Beyond Accessibility

In order to produce coherent text, natural language generation systems must have the ability to generate pronouns in the appropriate places. In the past, pronoun usage was primarily investigated with respect to the accessibility of referents. That is, it was assumed that a pronoun should be generated whenever the referent was sufficiently accessible so as to make its resolution easy. We found that such an explanation does not seem to account well for the patterns of pronoun usage found in naturally occurring texts. We present an algorithm for generating appropriate anaphoric expressions which takes into account the temporal structure of texts (as a discourse structuring device) and knowledge about ambiguous contexts. Other important factors in our algorithm are sentence boundaries and the distance from the last mention of the anaphor. We back up our hypotheses with some empirical results indicating that our algorithm chooses the right referring expression in 85% of the cases.
Restarting Frank-Wolfe

Conditional Gradients (aka Frank-Wolfe algorithms) form a classical set of methods for constrained smooth convex minimization due to their simplicity, the absence of projection step, and competitive numerical performance. While the vanilla Frank-Wolfe algorithm only ensures a worst-case rate of $O(1/\epsilon)$, various recent results have shown that for strongly convex functions, the method can be slightly modified to achieve linear convergence. However, this still leaves a huge gap between sublinear $O(1/\epsilon)$ convergence and linear $O(\log 1/\epsilon)$ convergence to reach an $\epsilon$-approximate solution. Here, we present a new variant of Conditional Gradients, that can dynamically adapt to the function's geometric properties using restarts and thus smoothly interpolates between the sublinear and linear regimes. Furthermore, our results apply to generic compact convex constraint sets.
Anomaly detection using Deep Autoencoders for the assessment of the quality of the data acquired by the CMS experiment

The certification of the CMS experiment data as usable for physics analysis is a crucial task to ensure the quality of all physics results published by the collaboration. Currently, the certification conducted by human experts is labor intensive and based on the scrutiny of distributions integrated on several hours of data taking. This contribution focuses on the design and prototype of an automated certification system assessing data quality on a per-luminosity section (i.e. 23 seconds of data taking) basis. Anomalies caused by detector malfunctioning or sub-optimal reconstruction are difficult to enumerate a priori and occur rarely, making it difficult to use classical supervised classification methods such as feedforward neural networks. We base our prototype on a semi-supervised approach which employs deep autoencoders. This approach has been qualified successfully on CMS data collected during the 2016 LHC run: we demonstrate its ability to detect anomalies with high accuracy and low false positive rate, when compared against the outcome of the manual certification by experts. A key advantage of this approach over other machine learning technologies is the great interpretability of the results, which can be further used to ascribe the origin of the problems in the data to a specific sub-detector or physics objects.
Zero-day recovery: the key to mitigating the ransomware threat

Combine zero-day exploits with ransomware and you have a cyberthreat that few organisations are equipped to handle. If all the focus is placed on cyber defence, what happens when those defences are breached? Financial harm, reputational damage and operational chaos commonly follow. But by bringing IT support and cyber security teams together, organisations can chart another way forward – a zero-day recovery architecture to transform ransomware from a critical business challenge to a mere irritant. Combine zero-day exploits with ransomware and you have a cyberthreat that few organisations are equipped to handle. If all the focus is placed on cyber defence, what happens when those defences are breached? Financial harm, reputational damage and operational chaos commonly follow. But by bringing IT support and cyber security teams together, organisations can chart another way forward – a zero-day recovery architecture to transform ransomware from a critical business challenge to a mere irritant, explains Alex Fagioli of Tectrade.