Reviewerly/exHarmony huggingface dataset:
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images

While neural symbolic methods demonstrate impressive performance in visual question answering on synthetic images, their performance suffers on real images. We identify that the long-tail distribution of visual concepts and unequal importance of reasoning steps in real data are the two key obstacles that limit the models’ real-world potentials. To address these challenges, we propose a new paradigm, Calibrating Concepts and Operations (CCO), which enables neural symbolic models to capture underlying data characteristics and to reason with hierarchical importance. Specifically, we introduce an executor with learnable concept embedding magnitudes for handling distribution imbalance, and an operation calibrator for highlighting important operations and suppressing redundant ones.Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images. By evaluating models on the real world dataset GQA, CCO helps the neural symbolic method NSCL outperforms its vanilla counterpart by 9.1% (from 47.0% to 56.1%); this result also largely reduces the performance gap between symbolic and non-symbolic methods. Additionally, we create a perturbed test set for better understanding and analyzing model performance on real images. Code is available at https://lizw14.github.io/project/ccosr.
Multiresolution Discriminative Mixup Network for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is a challenging task because there are many hard examples existing between fine-grained classes which differ subtly in particular local regions. To address this issue, many methods have recourse to high-resolution source images and others adopt effective regularization like “mixup” or “between class learning.” Despite their promising achievements, mixup tends to cause the manifold intrusion problem which would result in under-fitting and degradation of the model performance and high-resolution input inevitably leads to high computational costs. In view of this, we present a multiresolution discriminative mixup network (MRDMN). Different from standard mixup, the proposed discriminative mixup strategy mixes discriminative regions linearly instead of entire images to avoid manifold intrusion, which makes it learn the local detail features more effectively and contributes to more precise categorization. Furthermore, an innovative resolution-based distillation strategy is designed to transfer the multiresolution detail feature representations to a low-resolution network, which speeds up the testing and boosts the categorization accuracy simultaneously. Extensive experiments demonstrate that our proposed MRDMN remarkably outperforms most competitive approaches with less computation time on the CUB-200-2011, Stanford-Cars, Stanford-Dogs, Food-101, and iNaturalist 2017 datasets. The codes are in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aztc/MRDMN</uri> .
On the quantum performance evaluation of two distributed quantum architectures

Distributed quantum applications impose requirements on the quality of the quantum states that they consume. When analyzing architecture implementations of quantum hardware, characterizing this quality forms an important factor in understanding their performance. Fundamental characteristics of quantum hardware lead to inherent tradeoffs between the quality of states and traditional performance metrics such as throughput. Furthermore, any real-world implementation of quantum hardware exhibits time-dependent noise that degrades the quality of quantum states over time. Here, we study the performance of two possible architectures for interfacing a quantum processor with a quantum network. The first corresponds to the current experimental state of the art in which the same device functions both as a processor and a network device. The second corresponds to a future architecture that separates these two functions over two distinct devices. We model these architectures as Markov chains and compare their quality of executing quantum operations and producing entangled quantum states as functions of their memory lifetimes, as well as the time that it takes to perform various operations within each architecture. As an illustrative example, we apply our analysis to architectures based on Nitrogen-Vacancy centers in diamond, where we find that for present-day device parameters one architecture is more suited to computation-heavy applications, and the other for network-heavy ones. Besides the detailed study of these architectures, a novel contribution of our work are several formulas that connect an understanding of waiting time distributions to the decay of quantum quality over time for the most common noise models employed in quantum technologies. This provides a valuable new tool for performance evaluation experts, and its applications extend beyond the two architectures studied in this work.
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks

Gray-box graph attacks aim to disrupt the victim model's performance by using inconspicuous attacks with limited knowledge of the victim model. The details of the victim model and the labels of the test nodes are invisible to the attacker. The attacker constructs an imaginary surrogate model trained under supervision to obtain the gradient on the node attributes or graph structure. However, there is a lack of discussion on the training of surrogate models and the reliability of provided gradient information. The general node classification models lose the topology of the nodes on the graph, which is, in fact, an exploitable prior for the attacker. This paper investigates the effect of surrogate representation learning on the transferability of gray-box graph adversarial attacks. We propose Surrogate Representation Learning with Isometric Mapping (SRLIM) to reserve the topology in the surrogate embedding. By isometric mapping, our proposed SRLIM can constrain the topological structure of nodes from the input layer to the embedding space, that is, to maintain the similarity of nodes in the propagation process. Experiments prove the effectiveness of our approach through the improvement in the performance of the adversarial attacks generated by the gradient-based attacker in untargeted poisoning gray-box scenarios.
Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning

Given a question about an image, a Visual Commonsense Reasoning (VCR) model needs to provide not only a correct answer, but also a rationale to justify the answer. It is a challenging task due to the requirements of diverse visual content understanding, abstract language comprehending, and complicated inter-modality relationship reasoning. To solve above challenges, previous methods either resort to holistic attention mechanism or explore transformer-based model with pre-training, which, however, cannot perform comprehensive understanding and usually suffer from heavy computing burden. In this paper, we propose a novel multi-level counterfactual contrastive learning network for VCR by jointly modeling the hierarchical visual contents and the inter-modality relationships between the visual and linguistic domains. The proposed method enjoys several merits. First, with sufficient instance-level, image-level, and semantic-level contrastive learning, our model can extract discriminative features and perform comprehensive understanding for the image and linguistic expressions. Second, taking advantage of counterfactual thinking, we can generate informative factual and counterfactual samples for contrastive learning, resulting in stronger perception ability of our model. Third, an auxiliary contrast module is incorporated into our method to directly optimize the answer prediction in VCR, which further facilitates the representation learning. Extensive experiments on the VCR dataset demonstrate that our approach performs favorably against the state-of-the-arts.
Learning Order Parameters from Videos of Skyrmion Dynamical Phases with Neural Networks

The ability to recognize dynamical processes in physical events and to abstract physical concepts or even to reveal physical laws, lies at the core of human cognitive development. Modern machine learning methods have been given high hopes for realizing such ability, but are still in their infancy. Here we present our progress in this capability. The main purposes of this paper are to use neural networks for classifying the dynamical skyrmion phases of videos and to demonstrate that neural networks can learn physical concepts from the dynamical process. To this end, we employ multiple neural networks to recognize the static phases (image format) and dynamical phases (video format) of a particle-based skyrmion model. Our results show that neural networks, without any prior knowledge, can not only correctly classify these phases, but also predict the phase boundaries that agree with those obtained by simulation. We further propose a parameter visualization scheme to interpret what neural networks have learned. We show that neural networks can learn two order parameters from videos of dynamical phases and predict the critical values of two order parameters. Finally, we demonstrate that only two order parameters are needed to identify videos of skyrmion dynamical phases. Hence, this parameter visualization scheme can be used to determine how many order parameters are needed to fully recognize the input phases. Our work sheds light on the future use of neural networks in discovering meaningful physical concepts and revealing unknown yet physical laws from videos.
Deterministic multi-mode nonlinear coupling for quantum circuits

Abstract We present a general technique for deterministically implementing a multi-mode nonlinear coupling between several propagating microwave or optical modes in quantum circuits. The measurement induced technique combines specifically prepared resource states together with feasible feed-forward operations. We explore several ways of generating the suitable resource states and discuss their difference on an illustrative example of cubic coupling between two modes. We also show that the required entangled states with requisite nonlinear properties can be already generated in the present day experiments.
Universal linear optics by programmable multimode interference

We introduce a constructive algorithm for universal linear electromagnetic transformations between the N input and N output modes of a dielectric slab. The approach uses out-of-plane phase modulation programmed down to N2 degrees of freedom. The total area of these modulators equals that of the entire slab: our scheme makes optimal use of the available area for optical modulation. We also present error correction schemes that enable high-fidelity unitary transformations at large N. This "programmable multimode interferometer" (ProMMI) thus translates the algorithmic simplicity of Mach-Zehnder meshes into a holographically programmed slab, yielding DoF-limited compactness and error tolerance while eliminating the dominant sidewall-related optical losses and directional-coupler-related patterning challenges.
Classical Attacks on a Variant of the RSA Cryptosystem

Let \(N=pq\) be an RSA modulus with balanced prime factors. In 2018, Murru and Saettone presented a variant of the RSA cryptosystem based on a cubic Pell equation in which the public key (N, e) and the private key (N, d) satisfy \(ed\equiv 1\pmod {\left( p^2+p+1\right) \left( q^2+q+1\right) }\). They claimed that the classical small private attacks on RSA such as Wiener’s continued fraction attack do not apply to their scheme. In this paper, we show that, on the contrary, Wiener’s method as well as the small inverse problem technique of Boneh and Durfee can be applied to attack their scheme. More precisely, we show that the proposed variant of RSA can be broken if \(d<N^{0.5694}\). This shows that their scheme is in reality more vulnerable than RSA, where the bound of vulnerability is \(d<N^{0.292}\).
Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model's performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks.
Sense of online betrayal, brand hate, and outrage customers’ anti-brand activism

The current study develops a research model and explores the correlation between customer sense of online betrayal, brand hate, and anti-brand activism. The outrage customers’ anti-brand behaviors consist of negative online word of mouth, online public complaining, and online boycott. Data from an online survey of 383 online shoppers were used to test seven proposed hypotheses. The partial least square–structural equation modeling (PLS-SEM) was adopted to assess the measurement and structural model. The findings showed that the sense of online betrayal positively and significantly affects brand hate and anti-brand behaviors. In addition, brand hate is also the leading cause of customers’ anti-brand actions. The present study highlights the mediation role of brand hate in eliciting revenge from consumers subjected to online betrayal. This study also gives some recommendations to customers to stop the misconduct behaviors of online betrayals, such as spreading their betrayal cases to friends and relatives via social media, then asking for supports and help from governmental and legal agencies and participating in boycotts; raising boycott movements against the betraying brand should be considered as the most extreme punishment.
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer

Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents.However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentences in a document.We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document.Experimental results on the CNN/DailyMail dataset showed that NeRoBERTa outperforms baseline models in ROUGE.Human evaluation results also showed that NeRoBERTa achieves significantly better scores than the baselines in terms of coherence and yields comparable scores to the state-of-the-art models.
Three-dimensional vibration patterns of alto saxophone reeds measured on different mouthpieces under mimicked realistic playing conditions

In single reed musical instruments, vibrations of the reed, in conjunction with the geometry of the mouthpiece and the acoustic feedback of the instrument, play an essential role in sound generation. Up until now, three-dimensional (3D) reed vibration patterns have only been studied under external acoustic stimulation, or at a single note and lip force. This paper investigates vibration patterns of saxophone reeds under imitated realistic playing conditions. On different notes displacement measurements on the entire optically accessible part of the reed are performed using stroboscopic digital image correlation. These vibration data are decomposed onto the harmonic frequencies of the generated note pitch and into the operational modes. Motion data as a function of time are shown on single points. All points on the reed predominantly move in phase, corresponding to the first flexural mode of the reed. At higher note harmonics very low amplitude higher vibration modes are superimposed on the fundamental mode. Mouthpiece characteristics and lip force influence the vibration patterns. Vibration patterns differ strongly from earlier measurements on free vibrating reeds. Results show that single-point measurements on the tip of the reed can give a good indication of the 3D vibration amplitude, also at higher note pitches.
Health sciences librarians’ engagement in open science: a scoping review

Objectives: To identify the engagement of health sciences librarians (HSLs) in open science (OS) through the delivery of library services, support, and programs for researchers.Methods: We performed a scoping review guided by Arksey and O’Malley’s framework and Joanna Briggs’ Manual for Scoping Reviews. Our search methods consisted of searching five bibliographic databases (MEDLINE, Embase, CINAHL, LISTA, and Web of Science Core Collection), reference harvesting, and targeted website and journal searching. To determine study eligibility, we applied predetermined inclusion and exclusion criteria and reached consensus when there was disagreement. We extracted data in duplicate and performed qualitative analysis to map key themes.Results: We included fifty-four studies. Research methods included descriptive or narrative approaches (76%); surveys, questionnaires, and interviews (15%); or mixed methods (9%). We labeled studies with one or more of FOSTER's six OS themes: open access (54%), open data (43%), open science (24%), open education (6%), open source (6%), and citizen science (6%). Key drivers in OS were scientific integrity and transparency, openness as a guiding principle in research, and funder mandates making research publicly accessible.Conclusions: HSLs play key roles in advancing OS worldwide. Formal studies are needed to assess the impact of HSLs’ engagement in OS. HSLs should promote adoption of OS within their research communities and develop strategic plans aligned with institutional partners. HSLs can promote OS by adopting more rigorous and transparent research practices of their own. Future research should examine HSLs’ engagement in OS through social justice and equity perspectives.
Quantum Energy Lines and the Optimal Output Ergotropy Problem

We study the transferring of useful energy (work) along a transmission line that allows for partial preservation of quantum coherence. As a figure of merit we adopt the maximum values that ergotropy, total ergotropy, and nonequilibrium free energy attain at the output of the line for an assigned input energy threshold. For phase-invariant bosonic Gaussian channel (BGC) models, we show that coherent inputs are optimal. For (one-mode) not phase-invariant BGCs we solve the optimization problem under the extra restriction of Gaussian input signals.
Control cost and quantum speed limit time in controlled almost-exact state transmission in open systems

We investigate the influence of environment noise on the control cost and the quantum speed limit time (QSLT) in the process of almost-exact state transmission (AEST) through a spin chain under pulse control. The chain is immersed in its surrounding non-Markovian, finite-temperature heat baths. We find that AEST can be realized in weak system-bath coupling, low temperature, and strong non-Markovian baths under effective external control. Correspondingly, the control cost and QSLT increase with increasing bath temperature and coupling strength. It is noticeable that non-Markovianity from the baths can be helpful to reduce the control cost and shorten the QSLT. Furthermore, we find that there exists a trade-off between the control cost and transmission fidelity and that higher fidelity requires higher cost. In addition, the minimum control cost has been found to obtain certain transmission fidelity.
On the prediction of daily global solar radiation using temperature as input. An application of hybrid machine learners to the six climatic Moroccan zones

Forecasting of solar radiation intensity is a necessity for the establishment of solar energy projects and for decision-making in other related fields. Current prediction models/methods are site-dependent and their performance/accuracy outside the area of application is debatable. Temperature-based solar radiation models are highly recommended in areas where only air temperature data is available. Therefore, the purpose of this study is to evaluate the prediction accuracy of 42 existing temperature-based solar radiation models in forecasting the daily global solar radiation (DGSR) on horizontal surface for the six climatic zones of Morocco. In the first time, the models were assessed using only the least square method. Then, four Machine Learners models (SVM, Decision Tree, Gaussian Regression and Linear Regression) were employed as optimizers to improve the accuracy prediction of the models. The results differ from model to another based on their values of MBE, MSE, RMSE, σ and R2. Two methods were employed for ordering the studied models: the Performance score and the Taylor diagram. Long term meteorological data was used in the evaluation processes. The correlation R2 of the optimized models changes from 0.80 to 0.95 for all skies and from 0.95 to 0.98 for clear skies.
Development of Tele-Operated Underfloor Mobile Manipulator

In this study, we propose a tele-operated mobile manipulator for conducting underfloor work, including both inspection and repair. We focus on a caulking operation, and develop a mobile manipulator for finding and repairing cracks in a foundation. When designing the mobile manipulator, we consider a typical narrow underfloor environment, and perform detailed calculations. As a user interface, we furnish an image-pointing system for enabling us to operate the manipulator simply by clicking on a hand camera image. Then, we evaluate its performance based on navigation experiments, trajectory tracking experiments, and overall evaluation experiments.
Design and Implementation of Kubernetes enabled Federated Learning Platform

Federated learning (FL) is one of most promising distributed machine learning frameworks to strengthen data privacy and security by making. Specifically, in the FL, the multiple clients are involved to train their deep learning models (i.e., local training) and then aggregate them as a global model at the server. However, due to the heterogeneous nature of multiple distributed clients, it is challenging to implement and deploy the FL framework over various clients. By adopting the cloud native technologies such as containers, and Kubernetes, we design and implement the Kubernetes enabled Federated Learning Platform (KubeFL). The KubeFL helps the deployment of the FL over multiple clients via Docker containers and Kubernetes. To do this, in the proposed KubeFL, each FL framework implemented by the PyTorch is deployed on the Docker container in the Pod at each client (i.e., Node in the Kubernetes) while the one is also conducted at the server (i.e., Master in the Kubernetes). We evaluate its performance through extensive measurement-based analysis on the commercial NVIDIA Jetson TX2 edge devices under various practical configurations.