Reviewerly/exHarmony huggingface dataset:
Taming a nonconvex landscape with dynamical long-range order: Memcomputing Ising benchmarks

Recent work on quantum annealing has emphasized the role of collective behavior in solving optimization problems. By enabling transitions of clusters of variables, such solvers are able to navigate their state space and locate solutions more efficiently despite having only local connections between elements. However, collective behavior is not exclusive to quantum annealers, and classical solvers that display collective dynamics should also possess an advantage in navigating a non-convex landscape. Here, we give evidence that a benchmark derived from quantum annealing studies is solvable in polynomial time using digital memcomputing machines, which utilize a collection of dynamical components with memory to represent the structure of the underlying optimization problem. To illustrate the role of memory and clarify the structure of these solvers we propose a simple model of these machines that demonstrates the emergence of long-range order. This model, when applied to finding the ground state of the Ising frustrated-loop benchmarks, undergoes a transient phase of avalanches which can span the entire lattice and demonstrates a connection between long-range behavior and their probability of success. These results establish the advantages of computational approaches based on collective dynamics of continuous dynamical systems.
Dynamical structure factors of dynamical quantum simulators

Significance Quantum simulators promise to offer new insights into strongly correlated matter beyond what is accessible by means of classical computers. We propose dynamical quantum simulators (DQSs) as a method to simulate dynamical structure factors (DSFs) for system sizes considerably larger than what classical simulations can compute and provide complexity-theoretic evidence that they cannot be classically efficiently computed. Based on state-of-the-art experimental setups, we show how results from DQSs can be directly compared to experiments exploring properties of quantum materials. At the same time, we explore long-ranged spin systems: In particular, we show that the DSFs in DQSs can exhibit the signatures of excitation confinement in long-ranged models for which a comprehensive understanding is lacking.
Quantum State Engineering by Shortcuts to Adiabaticity in Interacting Spin-Boson Systems

We present a fast and robust framework to prepare non-classical states of a bosonic mode exploiting a coherent exchange of excitations with a two-level system ruled by a Jaynes-Cummings interaction mechanism. Our protocol, which is built on shortcuts to adiabaticity, allows for the generation of arbitrary Fock states of the bosonic mode, as well as coherent quantum superpositions of a Schr\"odinger cat-like form. In addition, we show how to obtain a class of photon-shifted states where the vacuum population is removed, a result akin to photon addition, but displaying more non-classicality than standard photon-added states. Owing to the ubiquity of the spin-boson interaction that we consider, our proposal is amenable for implementations in state-of-the-art experiments.
Generation and Processing of Simulated Underwater Images for Infrastructure Visual Inspection with UUVs

The development of computer vision algorithms for navigation or object detection is one of the key issues of underwater robotics. However, extracting features from underwater images is challenging due to the presence of lighting defects, which need to be counteracted. This requires good environmental knowledge, either as a dataset or as a physic model. The lack of available data, and the high variability of the conditions, makes difficult the development of robust enhancement algorithms. A framework for the development of underwater computer vision algorithms is presented, consisting of a method for underwater imaging simulation, and an image enhancement algorithm, both integrated in the open-source robotics simulator UUV Simulator. The imaging simulation is based on a novel combination of the scattering model and style transfer techniques. The use of style transfer allows a realistic simulation of different environments without any prior knowledge of them. Moreover, an enhancement algorithm that successfully performs a correction of the imaging defects in any given scenario for either the real or synthetic images has been developed. The proposed approach showcases then a novel framework for the development of underwater computer vision algorithms for SLAM, navigation, or object detection in UUVs.
Optimal short-term memory before the edge of chaos in driven random recurrent networks

The ability of discrete-time nonlinear recurrent neural networks to store time-varying small input signals is investigated with mean-field theory. The combination of a small input strength and mean-field assumptions makes it possible to derive an approximate expression for the conditional probability density of the state of a neuron given a past input signal. From this conditional probability density, we can analytically calculate short-term memory measures, such as memory capacity, mutual information, and Fisher information, and determine the relationships among these measures, which have not been clarified to date to the best of our knowledge. We show that the network contribution of these short-term memory measures peaks before the edge of chaos, where the dynamics of input-driven networks is stable but corresponding systems without input signals are unstable.
Simplified Fréchet Distance for Generative Adversarial Nets

We introduce a distance metric between two distributions and propose a Generative Adversarial Network (GAN) model: the Simplified Fréchet distance (SFD) and the Simplified Fréchet GAN (SFGAN). Although the data generated through GANs are similar to real data, GAN often undergoes unstable training due to its adversarial structure. A possible solution to this problem is considering Fréchet distance (FD). However, FD is unfeasible to realize due to its covariance term. SFD overcomes the complexity so that it enables us to realize in networks. The structure of SFGAN is based on the Boundary Equilibrium GAN (BEGAN) while using SFD in loss functions. Experiments are conducted with several datasets, including CelebA and CIFAR-10. The losses and generated samples of SFGAN and BEGAN are compared with several distance metrics. The evidence of mode collapse and/or mode drop does not occur until 3000k steps for SFGAN, while it occurs between 457k and 968k steps for BEGAN. Experimental results show that SFD makes GANs more stable than other distance metrics used in GANs, and SFD compensates for the weakness of models based on BEGAN-based network structure. Based on the experimental results, we can conclude that SFD is more suitable for GAN than other metrics.
DwNet: Dense warp-based network for pose-guided human video generation

Generation of realistic high-resolution videos of human subjects is a challenging and important task in computer vision. In this paper, we focus on human motion transfer - generation of a video depicting a particular subject, observed in a single image, performing a series of motions exemplified by an auxiliary (driving) video. Our GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose. Temporal consistency is maintained by further conditioning the decoding process within a GAN on the previously generated frame. In this way a video is generated in an iterative and recurrent fashion. We illustrate the efficacy of our approach by showing state-of-the-art quantitative and qualitative performance on two benchmark datasets: TaiChi and Fashion Modeling. The latter is collected by us and will be made publicly available to the community.
Dynamical Phase Transitions in Dissipative Quantum Dynamics with Quantum Optical Realization

We study dynamical phase transitions (DPT) in the driven and damped Dicke model, realizable for example by a driven atomic ensemble collectively coupled to a damped cavity mode. These DPTs are characterized by nonanalyticities of certain observables, primarily the overlap of time evolved and initial state. Even though the dynamics is dissipative, this phenomenon occurs for a wide range of parameters and no fine-tuning is required. Focusing on the state of the ``atoms'' in the limit of a bad cavity, we are able to asymptotically evaluate an exact path integral representation of the relevant overlaps. The DPTs then arise by minimization of a certain action function, which is related to the large deviation theory of a classical stochastic process. Finally, we present a scheme which allows a measurement of the DPT in a cavity-QED setup.
Constructing parsimonious analytic models for dynamic systems via symbolic regression

Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system’s behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit from the use of models, typically learned online. Any model construction method must address the tradeoff between the accuracy of the model and its complexity, which is difficult to strike. In this paper, we propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations. We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data: Single Node Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In addition to the standard problem formulation in the state-space domain, we show how the method can also be applied to input–output models of the NARX (nonlinear autoregressive with exogenous input) type. We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep neural networks and local linear regression shows that SR in most cases outperforms these commonly used alternative methods. We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples.
Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design

The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain's infrastructures and facilities based on customer demand. Key economic decisions are taken about the location, number, and size of manufacturing facilities and warehouses based on the optimal solution. Therefore, improvements in the methods to address this question, which is known to be in the NP-hard complexity class, would have relevant financial consequences. Here, we implement in the D-Wave quantum annealer a hybrid classical-quantum annealing algorithm. The cost function with constraints is translated to a spin Hamiltonian, whose ground state encodes the searched result. As a benchmark, we measure the accuracy of results for a set of paradigmatic problems against the optimal published solutions (the error is on average below $$1\%$$ ), and the performance is compared against the classical algorithm, showing a remarkable reduction in the number of iterations. This work shows that state-of-the-art quantum annealers may codify and solve relevant supply-chain problems even still far from useful quantum supremacy.
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.
Domain Adaptive Transfer Attack-Based Segmentation Networks for Building Extraction From Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).
Anti-locality and subject extraction

In many languages, Ā-extraction of local subject arguments behaves differently from the extraction of other arguments, for example in triggering specialized morphosyntactic processes or being subject to additional restrictions. I argue that many such interactions are due to an anti-locality constraint on movement, which bans movement which is too short. Subject extraction is often distinguished due to the high canonical position of subjects in their clauses (e.g. Spec,TP), making their movement to the clause edge (e.g. Spec,CP) uniquely in danger of violating the Spec-to-Spec Anti-Locality constraint (Erlewine 2016). Concretely, three subject extraction asymmetry behaviors are discussed and analyzed: complementizer-trace effects, subject anti-agreement effects, and bans on subject resumption, including the so-called Highest Subject Restriction. In each case, we observe that the special behavior associated with subject extraction (a) can be obviated by increasing the distance of movement, (b) also applies to exceptionally high non-subjects, and (c) does not correlate with other subjecthood properties. These facts are straightforwardly explained by the anti-locality-based approach to these asymmetries, but are challenging for alternative accounts.
Advances and Trends in Real Time Visual Crowd Analysis

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.
Asymmetric color image encryption and compression based on discrete cosine transform in Fresnel domain

In this paper, a new asymmetric method is presented to encrypt and compress color images using discrete cosine transform in Fresnel domain. First, the input color image is decomposed into three channels: red, green and blue. Each channel image is converted into phase image and bonded with an amplitude mask followed by Fresnel transform to get the complex intermediate image. Phase reservation (PR) and phase truncation (PT) operation are performed on the intermediate image. Thereafter, a random phase mask is bonded with the amplitude part and the phase part serves as of the first private key for decryption. The complex image is Fresnel propagated followed by the second PR and PT operation. Phase part serve as another private key and the discrete cosine transform is applied on the amplitude part of each red, green and blue channel to get the corresponding compressed encrypted images. The final compressed color encrypted image is obtained by combining the encrypted images of all three channels. In the decryption process, encrypted image is decomposed in red, green and blue channel and lost data in each channel is replaced by zeros. Each channel is subject to inverse discrete cosine transform and cascaded PT/PR operations with Fresnel propagation in reverse directions and recombined to get the original image. Numerical simulation results are presented to validate the proposed method. Also, the robustness of the proposed method is checked against various existing attacks and a comparative analysis confirms the effectiveness of our method.
Child–Computer Interaction in times of a pandemic

The increasing presence of interactive technologies in children’s lives poses critical ethical questions for researchers and designers. Discourse specific to these intersecting topics is nascent, spread across communities and largely developed retrospectively. In a series of workshops and panels at the annual ACM Interaction Design for Children (ACM IDC) conference, a small group from the community has aimed to provide a central space and continuity for this discourse. The most recent of these spaces was the ACM IDC 2020 workshop entitled, “Emergent, Situated and Prospective Ethics for Child–Computer Interaction Research” that brought together a small group of researchers interested in sharing and discussing ethical issues arising when researching, designing, and deploying technologies for children [1].When we, as the two main organizers of the workshop, wrote the workshop proposal we were particularly interested in how the ethical challenges in Child–Computer Interaction (CCI) were changing in the face of a rapidly changing world. At that time, it was completely unknown to us how different the world would be when we conducted the workshop. When the participants eventually met online for this workshop in late June 2020, a global pandemic had changed public life as we knew it and social justice issues, as manifested in the Black Lives Matter movement, were causing widespread unrest. Layered on these immediate challenges was the looming climate crisis that poses one of the most existential threats to humans yet.When we ran the workshop, we decided to focus our discussions on sharing thoughts and experiences of what these monumental shifts might mean for our community. The question, which we put onto the virtual floor was: How is the ethics of your research changing in the face of the Covid-19 Pandemic and recent social justice issues, as manifested in the Black Live Matters movement?What followed was a rich and thoughtful discussion. Every participant was affected by the pandemic in different ways, both personally and professionally. In the 45 min session we had reserved for this part of the workshop, we were all not only connected via video link, but also collaboratively worked on a shared virtual whiteboard at the same time. In a very productive mix of show and tell, we collected sticky notes, online resources, quotes and other bits of information; all the while making connections and categorizations. Later, we, as the two main organizers, discussed our observations from the workshop, and then reviewed the data and our own notes to informally look for themes related to our original question. We wrote up the themes individually, and worked collaboratively to refine, clarify, and contextualize them. From this exercise, four main themes emerged, which all included perceived challenges as well as the participants’ recommendations for how these concerns might be addressed moving forward. The following briefly summarizes these themes.
Wavelength-insensitive, multispecies entangling gate for group-2 atomic ions

We propose an optical scheme for generating entanglement between co-trapped identical or dissimilar alkaline-earth atomic ions ($^{40}\mathrm{Ca}^{+}$, $^{88}\mathrm{Sr}^{+}$, $^{138}\mathrm{Ba}^{+}$, $^{226}\mathrm{Ra}^{+}$) which exhibits fundamental error rates below ${10}^{\ensuremath{-}4}$ and can be implemented with a broad range of laser wavelengths spanning from ultraviolet to infrared. We also discuss straightforward extensions of this technique to include the two lightest group-2 ions (${\text{Be}}^{+}$, ${\text{Mg}}^{+}$) for multispecies entanglement. The key elements of this wavelength-insensitive geometric phase gate are the use of a ground (${S}_{1/2}$) and a metastable (${D}_{5/2}$) electronic state as the qubit levels within a ${\ensuremath{\sigma}}^{z}{\ensuremath{\sigma}}^{z}$ light-shift entangling gate. We present a detailed analysis of the principles and fundamental error sources for this gate scheme which includes photon scattering and spontaneous emission decoherence, calculating two-qubit-gate error rates and durations at fixed laser beam intensity over a large portion of the optical spectrum (300 nm to $2\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}\text{m}$) for an assortment of ion pairs. We contrast the advantages and disadvantages of this technique against previous trapped-ion entangling gates and discuss its applications to quantum information processing and simulation with like and multispecies ion crystals.