Reviewerly/exHarmony huggingface dataset:
Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images

In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recognize object instances using only image-level labels, which greatly saves the labeling costs of RSIs. In this article, we propose a self-directed weakly supervised strategy (SD-WSS) to perform WSOL in RSIs. To specify, we fully exploit and enhance the spatial feature extraction capability of the RSIs’ classification model to accurately localize the objects of interest. To alleviate the serious discriminative region problem exhibited by previous WSOL methods, the spatial location information implicit in the classification model is carefully extracted by GradCAM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$++$</tex-math> </inline-formula> to guide the learning procedure. Furthermore, to eliminate the interference from complex backgrounds of RSIs, we design a novel self-directed loss to make the model optimize itself and explicitly tell it where to look. Finally, we review and annotate the existing remote sensing scene classification dataset and create two new WSOL benchmarks in RSIs, named C45V2 and PN2. We conduct extensive experiments to evaluate the proposed method and six mainstream WSOL methods with three backbones on C45V2 and PN2. The results demonstrate that our proposed method achieves better performance when compared with state-of-the-arts.
Model-free tracking control of complex dynamical trajectories with machine learning

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing. Stochastic input is exploited for training, which consists of the observed partial state vector as the first and its immediate future as the second component so that the neural machine regards the latter as the future state of the former. In the testing (deployment) phase, the immediate-future component is replaced by the desired observational vector from the reference trajectory. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals, and establish its robustness against measurement noise, disturbances, and uncertainties.
Vegetation Evolution with Dynamic Maturity Strategy and Diverse Mutation Strategy for Solving Optimization Problems

We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems.
AI tools as science policy advisers? The potential and the pitfalls

Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers — but only with appropriate safeguards and humans in the loop. Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers — but only with appropriate safeguards and humans in the loop.
Embodying Multifunctional Mechano‐Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing

Recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific case studies and lacked a systematic foundation in effectively and efficiently constructing and integrating different intelligent functions. Here, a new approach is uncovered to create multifunctional MI in adaptive structures using physical reservoir computing (PRC). That is, to concurrently embody computing power and the key elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional reliance on add-on computers and massive electronics. As an exemplar platform, a mechanically intelligent phononic metastructure is developed by harnessing its high-degree-of-freedom nonlinear dynamics as PRC power. Through analyses and experiments, multiple intelligent structural functions are demonstrated ranging from self-tuning wave controls to wave-based logic gates. This research provides the much-needed basis for creating future smart structures and materials that greatly surpass the state of the art-such as lower power consumption, more direct interactions, and better survivability in harsh environments or under cyberattacks. Moreover, it enables the addition of new functions and autonomy to systems without overburdening the onboard computers.
TheoremQA: A Theorem-driven Question Answering Dataset

The recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. However, their capabilities to solve more challenging math problems which require domain-specific knowledge (i.e. theorem) have yet to be investigated. In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models’ capabilities to apply theorems to solve challenging science problems. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems from Math, Physics, EE&CS, and Finance. We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts. We found that GPT-4’s capabilities to solve these problems are unparalleled, achieving an accuracy of 51% with Program-of-Thoughts Prompting. All the existing open-sourced models are below 15%, barely surpassing the random-guess baseline. Given the diversity and broad coverage of TheoremQA, we believe it can be used as a better benchmark to evaluate LLMs’ capabilities to solve challenging science problems.
Influence of intrinsic decoherence on quantum metrology of two atomic systems in the presence of dipole–dipole interaction

Abstract We study the dynamics behavior of a two atomic qubits (two two-level atoms) exposed to a dipole–dipole interaction (DDI) under rotating wave approximation (RWA). Each atom resonantly interacts with the vacuum cavity field via one-photon hopping. We assume that the two atoms and field are initially prepared in their excited state and vacuum state, respectively. An exact solution of Milburn equation is given, and then we study a non-classical statistical properties in the full range of decoherence parameters. In the absence and presence of intrinsic decoherence, the influence of dipole–dipole interaction (DDI) on the degree of entanglement between atom-field and atom-atom are evaluated through different entanglement measures, namely, von Neumann reduced entropy and concurrence, respectively. The influence of DDI on geometrical quantum discord (GQD) are discussed. Furthermore, we estimate the sensitive of the atomic system under variation of DDI parameter via the quantum Fisher information.
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets

Managing heterogeneous datasets that vary in complexity, size, and similarity in continual learning presents a significant challenge. Task-agnostic continual learning is necessary to address this challenge, as datasets with varying similarity pose difficulties in distinguishing task boundaries. Conventional task-agnostic continual learning practices typically rely on rehearsal or regularization techniques. However, rehearsal methods may struggle with varying dataset sizes and regulating the importance of old and new data due to rigid buffer sizes. Meanwhile, regularization methods apply generic constraints to promote generalization but can hinder performance when dealing with dissimilar datasets lacking shared features, necessitating a more adaptive approach. In this article, we propose a novel adaptive continual learning (AdaptCL) method to tackle heterogeneity in sequential datasets. AdaptCL employs fine-grained data-driven pruning to adapt to variations in data complexity and dataset size. It also utilizes task-agnostic parameter isolation to mitigate the impact of varying degrees of catastrophic forgetting caused by differences in data similarity. Through a two-pronged case study approach, we evaluate AdaptCL on both datasets of MNIST variants and DomainNet, as well as datasets from different domains. The latter include both large-scale, diverse binary-class datasets and few-shot, multiclass datasets. Across all these scenarios, AdaptCL consistently exhibits robust performance, demonstrating its flexibility and general applicability in handling heterogeneous datasets.
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation

In this paper, we study the problem of end-to-end multi-person pose estimation. State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e.g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose [38], hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR [27].We present a simple yet effective transformer approach, named Group Pose. We simply regard K-keypoint pose estimation as predicting a set of N × K keypoint positions, each from a keypoint query, as well as representing each pose with an instance query for scoring N pose predictions.Motivated by the intuition that the interaction, among across-instance queries of different types, is not directly helpful, we make a simple modification to decoder self-attention. We replace single self-attention over all the N × (K + 1) queries with two subsequent group self-attentions: (i) N within-instance self-attention, with each over K keypoint queries and one instance query, and (ii) (K +1) same-type across-instance self-attention, each over N queries of the same type. The resulting decoder removes the interaction among across-instance type-different queries, easing the optimization and thus improving the performance. Experimental results on MS COCO and Crowd-Pose show that our approach without human box supervision is superior to previous methods with complex decoders, and even is slightly better than ED-Pose that uses human box supervision. Paddle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and PyTorch <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> codes are available.
An Expert System for Automatic Classiﬁcation of Sound Signals

In this paper, we present the results of research focusing on methods for recognition/classiﬁcation of audio signals. We consider the results of the research project to serve as a basis for the main module of a hybrid expert system currently under development. In our earlier studies, we conducted research on the eﬀectiveness of three classiﬁers: fuzzy classiﬁer, neural classiﬁer and WEKA system for reference data. In this project, a particular emphasis was placed on ﬁne-tuning the fuzzy classiﬁer model and on identifying neural classiﬁer applications, taking into account new neural networks that we have not studied so far in connection with sounds classiﬁcation methods
Nonlinearity of Boolean Functions: An Algorithmic Approach Based on Multivariate Polynomials

We review and compare three algebraic methods to compute the nonlinearity of Boolean functions. Two of them are based on Gröbner basis techniques: the first one is defined over the binary field, while the second one over the rationals. The third method improves the second one by avoiding the Gröbner basis computation. We also estimate the complexity of the algorithms, and, in particular, we show that the third method reaches an asymptotic worst-case complexity of O(n2n) operations over the integers, that is, sums and doublings. This way, with a different approach, the same asymptotic complexity of established algorithms, such as those based on the fast Walsh transform, is reached.
iBlogVis: An Interactive Blog Visualization Tool

We developed and evaluated a visualization tool for browsing individual blog archives. In our study, we solicited qualitative feedback from the participants to explore the reasons behind their usability ratings. This feedback reveals factors that are deemed important in selecting entries to read, and adds to the understanding of blogging practices. Based on our analysis, we propose two design principles to complement the current blog interfaces: (1) A blog should provide a rich overview of its content to ease information exploration; and (2) A blog should utilize social interaction history to help users find potentially useful entries.
Quantum phase estimation with squeezed quasi-Bell states

In this paper we present a study of the quantum phase estimation problem employing continuous-variable, entangled squeezed coherent (quasi-Bell) states as probe states. We show that their inherent squeezing and entanglement properties might bring advantages, increasing the precision of phase estimation compared to protocols which employ other continuous variable states e.g., two-mode, entangled coherent states or single-mode, squeezed states. We also analyze the phase estimation process considering: i) a linear (unitary) perturbation, and ii) dissipation, and conclude that the use of entangled squeezed coherent states as probe states may still be advantageous even under non-ideal conditions.
Persistency of genuine correlations under particle loss

In a recent work [\href{http://journals.aps.org/pra/abstract/10.1103/PhysRevA.86.042113}{Phys. Rev. A 86, 042113 (2012)}] the question of persistency of entanglement and nonlocality of multi-party systems under particle loss has been addressed. This question is of immense importance considering the practical realization of the information theoretic tasks which make use of the power of quantum correlations. But in multipartite scenario more interesting cases arise since subsystems can also possess genuineness in correlation which is prevalently inequivalent to the bipartite scenario. In this work, we investigate the persistency of such genuine correlations under particle loss. Keeping in mind the practical importance, considerable attention has been devoted to find the multipartite states which exhibit maximal persistency of genuine correlations.
Gathering Project Requirements: A Collaborative And Interdisciplinary Experience

Milwaukee School of Engineering has one of the first ABET-accredited undergraduate software engineering (SE) programs in the United States.As part of the curriculum, SE students are exposed to Requirements Engineering (RE) in their junior year.These concepts are reinforced through a quarter-long project in which the SE student teams work with clients who have product domain knowledge but often no formal experience in RE.Working in unfamiliar domains, being cognizant of ethical issues, and having to deal with ambiguous and conflicting customer requirements are some of the challenges that students face in a course like this.The authors have been working on a collaborative experiment where the clients for the junior SE student teams are biomedical engineering (BE) student design teams.This allows interdisciplinary collaboration, exposes the SE students to eliciting requirements in an unfamiliar domain, and exposes the BE students to a formal requirements process.The
Constant-Round Interactive Proof Systems for AC0[2] and NC1

We present constant-round interactive proof systems for sufficiently uniform versions of $$\mathcal{AC}^0[2]$$ and $$\mathcal{NC}^1$$. Both proof systems are doubly-efficient, and offer a better trade-off between the round complexity and the total communication than the work of Reingold, Rothblum, and Rothblum (STOC, 2016). Our proof system for $$\mathcal{AC}^0[2]$$ supports a more relaxed notion of uniformity and offers a better trade-off between the number of rounds and the round complexity that our proof system for $$\mathcal{NC}^1$$. We observe that all three aforementioned systems yield constant-round doubly-efficient proof systems for the All-Pairs Shortest Paths problem.
On invariance and linear convergence of evolution strategies with augmented Lagrangian constraint handling

In the context of numerical constrained optimization, we investigate stochastic algorithms, in particular evolution strategies, handling constraints via augmented Lagrangian approaches. In those approaches, the original constrained problem is turned into an unconstrained one and the function optimized is an augmented Lagrangian whose parameters are adapted during the optimization. The use of an augmented Lagrangian however breaks a central invariance property of evolution strategies, namely invariance to strictly increasing transformations of the objective function. We formalize nevertheless that an evolution strategy with augmented Lagrangian constraint handling should preserve invariance to strictly increasing affine transformations of the objective function and the scaling of the constraints—a subclass of strictly increasing transformations. We show that this invariance property is important for the linear convergence of these algorithms and show how both properties are connected.
Codes in a Dihedral Group Algebra

In 1978, Robert McEliece constructed the first asymmetric code-based cryptosystem using noise-immune Goppa codes; no effective key attacks has been described for it yet. By now, quite a lot of code-based cryptosystems are known; however, their cryptographic security is inferior to that of the classical McEliece cryptosystem. In connection with the development of quantum computing, code-based cryptosystems are considered as an alternative to number theoretical ones; therefore, the problem of seeking promising classes of codes to construct new secure code-based cryptosystems is relevant. For this purpose, noncommutative codes can be used, that is, ideals in group algebras $${{\mathbb{F}}_{q}}G$$ over finite noncommutative groups $$G$$. The security of cryptosystems based on codes induced by subgroup codes has been studied earlier. The Artin–Wedderburn theorem, which proves the existence of an isomorphism of a group algebra to the direct sum of matrix algebras, is important for studying noncommutative codes. However, the particular form of terms and the construction of the isomorphism are not specified by this theorem; thus, for each group, there remains the problem of constructing the Wedderburn representation. The complete Wedderburn decomposition for the group algebra $${{\mathbb{F}}_{q}}{{D}_{{2n}}}$$ over the dihedral group $${{D}_{{2n}}}$$ has been obtained by F.E. Brochero Martinez in the case when the cardinality of the field and the order of the group are relatively prime numbers. Using these results, we study codes in the group algebra $${{\mathbb{F}}_{q}}{{D}_{{2n}}}$$ in this paper. The problem on the structure of all codes is solved, and the structure of codes induced by codes over cyclic subgroups of $${{D}_{{2n}}}$$ is described, which is of interest for cryptographic applications.
Novel Formulation of Parzen Data Analysis

The Parzen analysis associates Gaussian kernels with each data point, thus obtaining a density function which may be viewed as a possible artificial generator of the data.This probability function can be decomposed into the product of two components, weight (W) and shape (S), which represent different aspects of the data.We demonstrate how this naturally leads to a formalism of fields in data space, which are interconnected through relations in one-dimensional scale space, corresponding to the common Gaussian width.We discuss the connection of this formalism to different clustering procedures such as quantum clustering (QC) and mean shift (MS).We demonstrate on various examples the importance of these concepts in the analysis of natural data as well as in image analysis in two or three dimensions.
Scalable State Space Search with Structural-Bottleneck Heuristics for Declarative IT System Update Automation

As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.