Reviewerly/exHarmony huggingface dataset:
Could an artificial-intelligence agent pass an introductory physics course?

Massive pretrained language models have garnered attention and controversy due to their ability to generate humanlike responses: Attention due to their frequent indistinguishability from human-generated phraseology and narratives and controversy due to the fact that their convincingly presented arguments and facts are frequently simply false. Just how humanlike are these responses when it comes to dialogues about physics, in particular about the standard content of introductory physics courses? This case study explores that question by having ChatGPT, the preeminent language model in 2023, work through representative assessment content of an actual calculus-based physics course and grading the responses in the same way human responses would be graded. As it turns out, ChatGPT would narrowly pass this course while exhibiting many of the preconceptions and errors of a beginning learner. A discussion of possible consequences for teaching, testing, and physics education research is provided as a possible starter for more detailed studies and curricular efforts in the future.5 MoreReceived 2 February 2023Accepted 21 March 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.010132Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentScientific reasoning & problem solvingTechnologyProfessional TopicsLower undergraduate studentsPhysics Education Research
Ultrastrongly dissipative quantum Rabi model

We discuss both the spectrum and the dynamics of cavity QED in the presence of dissipation beyond the standard perturbative treatment of losses. Using the dynamical polaron ansatz and matrix-product state simulations, we discuss the case where both light-matter $g$ coupling and system-bath interaction are in the ultra-strong-coupling regime. We provide a critical $g$ for the onset of Rabi oscillations. Besides, we demonstrate that the qubit is dressed by the cavity and dissipation. Such a dressing governs the dynamics and, thus, it can be measured. Finally, we sketch an implementation for our theoretical ideas within circuit QED technology.
Refacing: Reconstructing Anonymized Facial Features Using GANS

Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.
Tunable space-time crystal in room-temperature magnetodielectrics

We report the experimental realization of a space-time crystal with tunable periodicity in time and space in the magnon Bose-Einstein Condensate (BEC), formed in a room-temperature Yttrium Iron Garnet (YIG) film by radio-frequency space-homogeneous magnetic field. The magnon BEC is prepared to have a well defined frequency and non-zero wavevector. We demonstrate how the crystalline "density" as well as the time and space textures of the resulting crystal may be tuned by varying the experimental parameters: external static magnetic field, temperature, thickness of the YIG film and power of the radio-frequency field. The proposed space-time crystals provide a new dimension for exploring dynamical phases of matter and can serve as a model nonlinear Floquet system, that brings in touch the rich fields of classical nonlinear waves, magnonics and periodically driven systems.
Convolutional Neural Networks for ATC Classification

Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult.In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule.Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem.Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the "absolute true" rate and the "absolute false" rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.
Denoising of MR images using Kolmogorov-Smirnov distance in a Non Local framework

Data coming from any acquisition system, such as Magnetic Resonance Imaging ones, are affected by noise. Although modern high field scanners can reach high Signal to Noise Ratios, in some circumstances, for example in case of very weak signals due to a specific acquisition sequence, noise becomes a critical issue that has to be properly handled. In the last years methods based on the so called Non Local Mean have proven to be very effective in denoising tasks. The idea of these filters is to find similar patches across the image and to jointly exploit them to obtain the restored image. A critical point is the distance metric adopted for measuring similarity. Within this manuscript, we propose a filtering technique based on the Kolmogorov-Smirnov distance. The main innovative aspect of the proposed method consists of the criteria adopted for finding similar pixels across the image: it is based on the statistics of the points rather than the widely adopted weighted Euclidean distance. More in details, the Cumulative Distribution Functions of different pixels are evaluated and compared in order to measure their similarities, exploiting a stack of images of the same slice acquired with different acquisition parameters. To quantitatively and qualitatively assess the performances of the approach, a comparison with other widely adopted denoising filters in case of both simulated and real datasets has been carried out. The obtained results confirm the validity of the proposed solution.
Moment-Based 2.5-D Visual Servoing for Textureless Planar Part Grasping

Conventional moment-based visual servoing methods suffer from several problems in industrial applications due to the utilization of high-order image moments. In this paper, we analyze the shortcomings of the moment-based visual servoing from the viewpoint of practical industrial applications, and propose a novel moment-based two-and-a-half-dimensional visual servoing method for grasping textureless planar parts. We use hybrid visual features that combine image moments with three-dimensional (3-D) rotation in the Cartesian space to control the robot motion. Instead of applying high-order image moments, we use rotation features, which provide a decoupled interaction matrix that is full rank and with no local minimum in the control scheme. Furthermore, to estimate the relative rotation of the textureless part in real time, a new estimation method based on a cross-correlation analysis is proposed. The proposed visual servoing method provides a better motion control and 3-D trajectory of the robot arm and remains stable in the workspace. Experimental results demonstrate the effectiveness of the method.
Influence of pump coherence on the generation of position-momentum entanglement in optical parametric down-conversion

We examine experimentally how the degree of position-momentum entanglement of photon pairs depends on the transverse coherence of the pump beam that excites them in a process of spontaneous parametric down-conversion. Using spatially incoherent light from a light-emitting diode, we obtain strong position correlation of the photons, but we find that transverse momentum correlation, and thus entanglement, is entirely absent. When we continuously vary the degree of spatial coherence on the pump beam, we observe the emergence of stronger momentum correlations and entanglement. We present theoretical arguments that explain our experimental results. Our results shed light on entanglement generation and can be applied to control entanglement for quantum information applications.
A Unifying Bayesian View of Continual Learning

Some machine learning applications require continual learning - where data comes in a sequence of datasets, each is used for training and then permanently discarded. From a Bayesian perspective, continual learning seems straightforward: Given the model posterior one would simply use this as the prior for the next task. However, exact posterior evaluation is intractable with many models, especially with Bayesian neural networks (BNNs). Instead, posterior approximations are often sought. Unfortunately, when posterior approximations are used, prior-focused approaches do not succeed in evaluations designed to capture properties of realistic continual learning use cases. As an alternative to prior-focused methods, we introduce a new approximate Bayesian derivation of the continual learning loss. Our loss does not rely on the posterior from earlier tasks, and instead adapts the model itself by changing the likelihood term. We call these approaches likelihood-focused. We then combine prior- and likelihood-focused methods into one objective, tying the two views together under a single unifying framework of approximate Bayesian continual learning.
Object Detection in Aerial Images Using Feature Fusion Deep Networks

Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.
The effect of sustainability accounting disclosures on financial performance: an empirical study on the Jordanian banking sector

This study aims to examine the effect of sustainability accounting disclosure on the financial performance of banks operating in Jordan during the period of 2013–2017. The study focused on the effect of economic, environmental and social disclosures on financial performance. To achieve the study objective, the content analysis method was used. The dimensions of sustainability accounting disclosure were measured through indicators that have been developed for this purpose, which are in accordance with Global Reporting Initiative (GRI) (G4-2013). The study results revealed that there is a statistically significant effect of sustainability accounting disclosure on the financial performance. In addition, the study results revealed that the disclosure of the economic and social dimensions had a positive effect on return on equity (ROE). While the environmental dimension did not affect the return on equity (ROE). In addition, the results of the study revealed that the disclosure of sustainability dimensions (economic, social and environment) had a combined effect on the return on assets (ROA). This means that the banks operating in Jordan give more priority to the economic dimension disclosure, than to the social and environmental dimensions.In reviewing previous studies in the accounting literature, it has been found that there is a paucity of studies that examine the concept of sustainability accounting, especially in the Jordanian banking sector. Therefore, this study constitutes value to this field.
Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-Aware Traffic Management

The concept of Quality of Experience (QoE) of Internet services is widely recognized by service providers and network operators. They strive to deliver the best experience to their customers in order to increase revenues and avoid churn. Therefore, QoE is increasingly considered as an integral part of the reactive traffic management cycle of network operators. In addition, QoE also constitutes a cycle of its own, which includes user behavior and service requirements. This letter describes this QoE cycle, which is not widely taken into account yet, discusses the interactions of the two cycles, and derives implications toward an improved and proactive QoE-aware traffic management. A showcase on how network operators can obtain hints on the change of network requirements from detecting user behavior in encrypted video traffic is also presented.
Long short-term memory for a model-free estimation of macronutrient ion concentrations of root-zone in closed-loop soilless cultures

Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM.The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R2 = 0.84 and the test accuracy was R2 = 0.67 with RMSE = 1.48 meq L-1. The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation.We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment.
Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.
Quantum Channel Simulation and the Channel’s Smooth Max-Information

We study the general framework of quantum channel simulation, that is, the ability of a quantum channel to simulate another one using different classes of codes. First, we show that the minimum error of simulation and the one-shot quantum simulation cost under no-signalling assisted codes are given by semidefinite programs. Second, we introduce the channel's smooth max-information, which can be seen as a one-shot generalization of the mutual information of a quantum channel. We provide an exact operational interpretation of the channel's smooth max-information as the one-shot quantum simulation cost under no-signalling assisted codes, which significantly simplifies the study of channel simulation and provides insights and bounds for the case under entanglement-assisted codes. Third, we derive the asymptotic equipartition property of the channel's smooth max-information; i.e., it converges to the quantum mutual information of the channel in the independent and identically distributed asymptotic limit. This implies the quantum reverse Shannon theorem in the presence of no-signalling correlations. Finally, we explore the simulation cost of various quantum channels.
Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech

There is a growing interest in models that can learn from unlabelled speech paired with visual context. This setting is relevant for low-resource speech processing, robotics, and human language acquisition research. Here, we study how a visually grounded speech model, trained on images of scenes paired with spoken captions, captures aspects of semantics. We use an external image tagger to generate soft text labels from images, which serve as targets for a neural model that maps untranscribed speech to (semantic) keyword labels. We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query. Without seeing any text, the model trained on parallel speech and images achieves a precision of almost 60% on its top ten semantic retrievals. Compared to a supervised model trained on transcriptions, our model matches human judgements better by some measures, especially in retrieving non-verbatim semantic matches. We perform an extensive analysis of the model and its resulting representations.
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the contributions of individual users. Current practical protocols for secure aggregation work in an "honest but curious" setting where a curious adversary observing all communication to and from the server cannot learn any private information assuming the server is honest and follows the protocol. A more scalable and robust primitive for privacy-preserving protocols is shuffling of user data, so as to hide the origin of each data item. Highly scalable and secure protocols for shuffling, so-called mixnets, have been proposed as a primitive for privacy-preserving analytics in the Encode-Shuffle-Analyze framework by Bittau et al., which was later analytically studied by Erlingsson et al. and Cheu et al.. The recent papers by Cheu et al., and Balle et al. have given protocols for secure aggregation that achieve differential privacy guarantees in this "shuffled model". Their protocols come at a cost, though: Either the expected aggregation error or the amount of communication per user scales as a polynomial $n^{Ω(1)}$ in the number of users $n$. In this paper we propose simple and more efficient protocol for aggregation in the shuffled model, where communication as well as error increases only polylogarithmically in $n$. Our new technique is a conceptual "invisibility cloak" that makes users' data almost indistinguishable from random noise while introducing zero distortion on the sum.