Reviewerly/exHarmony huggingface dataset:
Fractional Fourier Transform Based Joint Adaptive Subspace Detection for Hyperspectral Anomaly Detection

The use of a transform domain is usually helpful to improve the performance of hyperspectral anomaly detection (AD). By fractional Fourier transform (FrFT), the signals obtain complementary characteristics in an intermediate domain between the original reflectance spectrum and its Fourier transform. In this letter, a joint adaptive subspace detection (ASD) model based on FrFT (FJASD) is proposed for hyperspectral AD. The FJASD has two parts. First, hyperspectral imagery (HSI) is transformed by FrFT and background joint sparse representation (BJSR) that is the ASD model based on the test point vector is used in the fractional Fourier domain (FrFD) for AD. This part mainly uses spectral characteristics of HSI. Then, the tensor block center at the test point in the original domain is transformed into a 1-D vector and dimensionally reduced by a principal component analysis (PCA). The lower dimensional dataset is transformed by FrFT and tensor-based ASD (TBASD) that is the ASD model based on the test point tensor is used in FrFD for AD. This part mainly uses the spatial characteristics of HSI. In addition, the spectral information after removing the redundancy is also utilized. Finally, the above two parts are combined by a weight coefficient and complement each other. Four comparison algorithms are used to verify the AD performance of FJASD through three hyperspectral datasets and the detection results show the superiority of the proposed FJASD.
Disconnectedness in a connected world: why people ignore messages and calls

Purpose People are living in a world where they maintain connectivity through sending and receiving messages and calls. Yet, almost daily, people choose not to respond to certain messages or calls, which can make the sender anxious, and adversely affect their communication. The aim of this study was to investigate the receivers' reasons for not responding. Design/methodology/approach The authors used theories related to information avoidance, power, interpersonal deception and emotions and conducted interviews of smartphone users. Findings The study found that the receiver’s physical and psychological state, the time of the day and the content of the message impacted non-response. The findings suggest that the non-response behavior is moderated by the power relationship between the sender and the receiver. The receiver’s state of mind will determine the likelihood of non-response, while the sender’s state of mind will determine how the sender deals with non-response. Research limitations/implications The study contributes to research in mobile information behavior, and the wider fields of information science, sociology and communication. Practical implications The process of interviewing itself helped raise awareness about these issues with the people who were interviewed. Social implications The findings shed light on the current communicative practices and ways to overcome the disconnectedness and stress suffered by people regularly using smartphones. Originality/value The study provides recommendations for healthy communication between the sender and the receiver using their smartphones.
Optically initialized and current-controlled logical element based on antiferromagnetic-heavy metal heterostructures for neuromorphic computing

A concept of optically triggered and electrically controlled ultra-fast neuromorphic computing processor based on an antiferromagnetic/heavy metal (AFM/HM) heterostructure is proposed. The AFM/HM-based artificial neurons are excited with short THz-range pulses, triggering precession in AFM. Bias electric current in the HM layer can be used to modify the resonance frequency of precession. The conversion of the precession into the electric current in the HM-layer occurs via the inverse spin Hall effect. A model of a neuromorphic processor is, thus, proposed, consisting of excitatory AFM-based artificial neurons—oscillators, and processing neurons—detectors. We show that the use of optical excitation can significantly increase the processing speed of neuromorphic computing at low power consumption. Examples of the implementation of the simplest logical operations (OR, AND) are demonstrated.
Fair Graph Representation Learning with Imbalanced and Biased Data

Graph-structured data is omnipresent in various fields, such as biology, chemistry, social media and transportation. Learning informative graph representations are crucial in effectively completing downstream graph-related tasks such as node/graph classification and link prediction. Graph Neural Networks (GNNs), due to their inclusiveness on handling graph-structured data and distinguished data-mining power inherited from deep learning, have achieved significant success in learning graph representations. Nonetheless, most existing GNNs are mainly designed with unrealistic data assumptions, such as the balanced and unbiased data distributions while abounding real-world networks exhibit skewed (i.e., long-tailed) node/graph class distributions and may also encode patterns of previous discriminatory decisions dominated by sensitive attributes. Even further, extensive research efforts have been invested in developing GNN architectures towards improving model utility while most of the time totally ignoring whether the obtained node/graph representations conceal any discriminatory bias, which could lead to prejudicial decisions as GNN-based machine learning models are increasingly being utilized in real-world applications. In light of the prevalence of the above two types of unfairness originated from quantity-imbalanced and discriminatory bias, my research expects to propose novel node/graph representation learning frameworks through constructing innovative GNN architectures and devising novel graph-mining algorithms to learn both fair and expressive node/graph representations that can enjoy a favorable fairness-utility tradeoff.
Agile Beyond Teams and Feedback Beyond Software in Automotive Systems

In order to increase the ability to build complex, software-intensive systems, as well as to decrease time-to-market for new functionality, automotive companies aim to scale agile methods beyond individual teams. This is challenging, given the specifics of automotive systems that are often safety-critical and consist of software, hardware, and mechanical components. This paper investigates the concrete reasons for scaling agility beyond teams, the strategies that support such scaling, and foreseeable implications that such a drastic organizational change will entail. The investigation is based on a qualitative case study, with data from 20 semi-structured interviews with managers and technical experts at two automotive companies. At the core of our findings are observations about establishing an agile vehicle-level feedback loop beyond individual teams. (I) We find that automotive OEMs aim to decrease lead-time of development. (II) We also identify 7 strategies that aim to enable scaled-agile beyond teams. (III) Finally, we extract 6 foreseeable implications and side-effects of scaling agile beyond teams in automotive. By charting the landscape of expected benefits, strategies, and implications of scaling agile beyond teams in automotive, we enable further research and process improvements.
Flexible lateral shearing interferometry based on polarization gratings for surface figure metrology

A lateral shearing interferometer based on polarization gratings to measure surface figures is proposed and experimentally verified. The birefringent, polarizing beam splitting characteristics of polarization gratings make the optical configuration compact and flexible to adjust the lateral shear. The use of a polarization camera does not require sequential measurements, enabling single shot phase derivative characterization. In the experimental results, various specimens including freeform surfaces were measured with less than 100 nm deviations from the references.
Hyperspectral Image Denoising Using Nonconvex Local Low-Rank and Sparse Separation With Spatial–Spectral Total Variation Regularization

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel $\ell_{2,\log}$ norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the $\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.
Maximal coin-walker entanglement in a ballistic quantum walk

We report that the position-inhomogeneous quantum walk (IQW) can be utilized to produce the maximal high-dimensional entanglement while maintaining the quadratic speedup spread of the wave function. Our calculations show that the maximal coin-walker entanglement can be generated in any odd steps or asymptotically in even steps, and the nearly maximal entanglement can be obtained in even steps after 2. We implement the IQW by a stable resource-saving time-bin optical network, in which a polarization Sagnac loop is employed to realize the precisely tunable phase shift. Our approach opens up an efficient way for high-dimensional entanglement engineering as well as promotes investigations on the role of coin-walker interactions in QW-based applications.
Quantum and classical annealing in a continuous space with multiple local minima

The protocol of quantum annealing is applied to an optimization problem with a one-dimensional continuous degree of freedom, a variant of the problem proposed by Shinomoto and Kabashima. The energy landscape has a number of local minima, and the classical approach of simulated annealing is predicted to have a logarithmically slow convergence to the global minimum. We show by extensive numerical analyses that quantum annealing yields a power law convergence, thus an exponential improvement over simulated annealing. The power is larger, and thus the convergence is faster, than a prediction by an existing phenomenological theory for this problem. Performance of simulated annealing is shown to be enhanced by introducing quasi-global searches across energy barriers, leading to a power-law convergence but with a smaller power than in the quantum case and thus a slower convergence classically even with quasi-global search processes. We also reveal how diabatic quantum dynamics, quantum tunneling in particular, steers the systems toward the global minimum by a meticulous choice of annealing schedule. This latter result explicitly contrasts the role of tunneling in quantum annealing against the classical counterpart of stochastic optimization by simulated annealing.
SSR-HEF: Crowd Counting With Multiscale Semantic Refining and Hard Example Focusing

Crowd counting based on density maps is generally regarded as a regression task.Deep learning is used to learn the mapping between image content and crowd density distribution. Although great success has been achieved, some pedestrians far away from the camera are difficult to be detected. And the number of hard examples is often larger. Existing methods with simple Euclidean distance algorithm indiscriminately optimize the hard and easy examples so that the densities of hard examples are usually incorrectly predicted to be lower or even zero, which results in large counting errors. To address this problem, we are the first to propose the Hard Example Focusing(HEF) algorithm for the regression task of crowd counting. The HEF algorithm makes our model rapidly focus on hard examples by attenuating the contribution of easy examples.Then higher importance will be given to the hard examples with wrong estimations. Moreover, the scale variations in crowd scenes are large, and the scale annotations are labor-intensive and expensive. By proposing a multi-Scale Semantic Refining (SSR) strategy, lower layers of our model can break through the limitation of deep learning to capture semantic features of different scales to sufficiently deal with the scale variation. We perform extensive experiments on six benchmark datasets to verify the proposed method. Results indicate the superiority of our proposed method over the state-of-the-art methods. Moreover, our designed model is smaller and faster.
Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention

An avatar mirroring the user's movement is commonly adopted in Virtual Reality(VR). Maintaining the user-avatar movement consistency provides the user a sense of body ownership and thus an immersive experience. However, breaking this consistency can enable new interaction functionalities, such as pseudo haptic feedback [45] or input augmentation [37, 59], at the expense of immersion. We propose to quantify the probability of users noticing the movement inconsistency while the inconsistency amplitude is being enlarged, which aims to guide the intervention of the users' sense of body ownership in VR. We applied angular offsets to the avatar's shoulder and elbow joints and recorded whether the user identified the inconsistency through a series of three user studies and built a statistical model based on the results. Results show that the noticeability of movement inconsistency increases roughly quadratically with the enlargement of offsets and the offsets at two joints negatively affect the probability distributions of each other. Leveraging the model, we implemented a technique that amplifies the user's arm movements with unnoticeable offsets and then evaluated implementations with different parameters(offset strength, offset distribution). Results show that the technique with medium-level and balanced-distributed offsets achieves the best overall performance. Finally, we demonstrated our model's extendability in interventions in the sense of body ownership with three VR applications including stroke rehabilitation, action game and widget arrangement.
Numerical analysis of quantum circuits for state preparation and unitary operator synthesis

We perform optimal-control-theory calculations to determine the minimum number of two-qubit controlled-not (cnot) gates needed to perform quantum state preparation and unitary operator synthesis for few-qubit systems. By considering all possible gate configurations, we determine the maximum achievable fidelity as a function of quantum circuit size. This information allows us to identify the minimum circuit size needed for a specific target operation and enumerate the different gate configurations that allow a perfect implementation of the operation. We find that there are a large number of configurations that all produce the desired result, even at the minimum number of gates. We also show that the number of entangling gates can be reduced if we use multiqubit entangling gates instead of two-qubit cnot gates, as one might expect based on parameter counting calculations. In addition to treating the general case of arbitrary target states or unitary operators, we apply the numerical approach to the special case of synthesizing the multiqubit Toffoli gate. This approach can be used to investigate any other specific few-qubit task and provides insight into the tightness of different bounds in the literature.
Bending the rules of low-temperature thermometry with periodic driving

There exist severe limitations on the accuracy of low-temperature thermometry, which poses a major challenge for future quantum-technological applications. Low-temperature sensitivity might be manipulated by tailoring the interactions between probe and sample. Unfortunately, the tunability of these interactions is usually very restricted. Here, we focus on a more practical solution to boost thermometric precision -- driving the probe. Specifically, we solve for the limit cycle of a periodically modulated linear probe in an equilibrium sample. We treat the probe-sample interactions \textit{exactly} and hence, our results are valid for arbitrarily low temperatures $ T $ and any spectral density. We find that weak near-resonant modulation strongly enhances the signal-to-noise ratio of low-temperature measurements, while causing minimal back action on the sample. Furthermore, we show that near-resonant driving changes the power law that governs thermal sensitivity over a broad range of temperatures, thus `bending' the fundamental precision limits and enabling more sensitive low-temperature thermometry. We then focus on a concrete example -- impurity thermometry in an atomic condensate. We demonstrate that periodic driving allows for a sensitivity improvement of several orders of magnitude in sub-nanokelvin temperature estimates drawn from the density profile of the impurity atoms. We thus provide a feasible upgrade that can be easily integrated into low-$T$ thermometry experiments.
Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference

Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference implementations that are efficient enough is challenging for many real-world problems. Often, this is due to PPLs not fully exploiting available parallelization and optimization opportunities. For example, handling probabilistic checkpoints in PPLs through continuation-passing style transformations or non-preemptive multitasking -- as is done in many popular PPLs -- often disallows compilation to low-level languages required for high-performance platforms such as GPUs. To solve the checkpoint problem, we introduce the concept of PPL control-flow graphs (PCFGs) -- a simple and efficient approach to checkpoints in low-level languages. We use this approach to implement RootPPL: a low-level PPL built on CUDA and C++ with OpenMP, providing highly efficient and massively parallel SMC inference. We also introduce a general method of compiling universal high-level PPLs to PCFGs and illustrate its application when compiling Miking CorePPL -- a high-level universal PPL -- to RootPPL. The approach is the first to compile a universal PPL to GPUs with SMC inference. We evaluate RootPPL and the CorePPL compiler through a set of real-world experiments in the domains of phylogenetics and epidemiology, demonstrating up to 6x speedups over state-of-the-art PPLs implementing SMC inference.
EvoJAX

Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters. Recent work have also showcased their effectiveness on hardware accelerators, such as GPUs, but so far such demonstrations are tailored for very specific tasks, limiting applicability to other domains. We present EvoJAX, a scalable, general purpose, hardware-accelerated neuroevolution toolkit. Building on top of the JAX library, our toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs. EvoJAX achieves very high performance by implementing the evolution algorithm, neural network and task all in NumPy, which is compiled just-in-time to run on accelerators. We provide extensible examples of EvoJAX for a wide range of tasks, including supervised learning, reinforcement learning and generative art. Since EvoJAX can find solutions to most of these tasks within minutes on a single accelerator, compared to hours or days when using CPUs, our toolkit can significantly shorten the iteration cycle of evolutionary computation experiments. EvoJAX is available at https://github.com/google/evojax
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

Recently, computer vision foundation models such as CLIP and ALI-GN, have shown impressive generalization capabilities on various downstream tasks. But their abilities to deal with the long-tailed data still remain to be proved. In this work, we present a novel framework based on pre-trained visual-linguistic models for long-tailed recognition (LTR), termed VL-LTR, and conduct empirical studies on the benefits of introducing text modality for long-tailed recognition tasks. Compared to existing approaches, the proposed VL-LTR has the following merits. (1) Our method can not only learn visual representation from images but also learn corresponding linguistic representation from noisy class-level text descriptions collected from the Internet; (2) Our method can effectively use the learned visual-linguistic representation to improve the visual recognition performance, especially for classes with fewer image samples. We also conduct extensive experiments and set the new state-of-the-art performance on widely-used LTR benchmarks. Notably, our method achieves 77.2% overall accuracy on ImageNet-LT, which significantly outperforms the previous best method by over 17 points, and is close to the prevailing performance training on the full ImageNet. Code is available at https://github.com/ChangyaoTian/VL-LTR .
Blockchain Technology as a Factor Affecting the Digitalization of the Financial Sector

The current development of the global economy indicates the inefficiency of some financial mechanisms which encourages market participants to implement new technologies intended to change the financial sector and the economy as a whole by transforming business models and attracting new partners. One of the promising technologies that can have a significant impact on the financial and economic processes is blockchain. Issues affecting blockchain applications in the financial, management, logistics and other sectors and impacts of this technology on their efficiency require further research. The article aims to analyze blockchain applications in the financial sector, the dynamics of indicators describing the level of penetration of this technology in the banking sector and tools used to develop a digital infrastructure in this area. The article describes blockchain applications in credit institutions, taking into account advantages of this technology. The model of blockchain applications in the financial sector aimed at improving the use of digital technology is also described. Possible options for further development of the financial sector using blockchain are analyzed.