Reviewerly/exHarmony huggingface dataset:
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.
Advances in quantum entanglement purification

Since its discovery, the quantum entanglement becomes a promising resource in quantum communication and computation. However, the entanglement is fragile due to the presence of noise in quantum channels. Entanglement purification is a powerful tool to distill high quality entangled states from the low quality entangled states. In this review, we present an overview of entanglement purification, including the basic entanglement purification theory, the entanglement purification protocols (EPPs) with linear optics, EPPs with cross-Kerr nonlinearities, hyperentanglement EPPs, deterministic EPPs, and measurement-based EPPs. We also review experimental progresses of EPPs in linear optics. Finally, we give the discussion on potential outlook for the future development of EPPs. This review may pave the way for practical implementations in future long-distance quantum communication and quantum network.
A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: A design and implementation study

B-cell acute lymphoblastic leukemia (B-ALL) is one of the most widespread cancers, and its definitive diagnosis demands invasive and costly diagnostic tests with side effects for patients. Access to definitive diagnostic equipment for B-ALL is limited in many geographical areas. Blood microscopic examination has always been a major B-ALL screening and diagnosis technique. Still, the examination of blood microscopically by laboratory personnel and hematologists is riddled with disadvantages. Meanwhile, AI techniques can achieve remarkable results in blood microscopy image analysis. The present study aimed to design and implement a well-tuned based on deep CNN to detect B-ALL cases from hematogones and then determine the B-ALL subtype. Based on the well-designed and tuned model, a mobile application was also designed for screening B-ALL from non-B-ALL cases. In the modeling stage, a unique segmentation technique was used for color thresholding in the color LAB space. By applying the K-means clustering algorithm, and adding a mask to the clustered images, a segmented image was obtained to eliminate unnecessary components. After comparing the efficiency of three notable architectures of lightweight CNN (EfficientNetB0, MobileNetV2, and NASNet Mobile), the most efficient model was selected, and the proposed model was accordingly configured and tuned. The proposed model achieved an accuracy of 100%. Finally, a mobile application was designed based on this state-of-the-art model. In the real laboratory setting, the mobile application based on the proposed model classified B-ALL cases from other classes and achieved a sensitivity and specificity of 100% as a robust screening tool. The application that relies on preprocessing and DL algorithms can be used as a powerful screening tool by hematologists and clinical specialists to ignore or minimize unnecessary bone marrow biopsy cases and decrease the B-ALL diagnosis time.
Algorithms to estimate Shapley value feature attributions

Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both theoretical and computational standpoints. We disentangle this complexity into two main factors: the approach to removing feature information and the tractable estimation strategy. These two factors provide a natural lens through which we can better understand and compare 24 distinct algorithms. Based on the various feature-removal approaches, we describe the multiple types of Shapley value feature attributions and the methods to calculate each one. Then, based on the tractable estimation strategies, we characterize two distinct families of approaches: model-agnostic and model-specific approximations. For the model-agnostic approximations, we benchmark a wide class of estimation approaches and tie them to alternative yet equivalent characterizations of the Shapley value. For the model-specific approximations, we clarify the assumptions crucial to each method’s tractability for linear, tree and deep models. Finally, we identify gaps in the literature and promising future research directions. There are numerous algorithms for generating Shapley value explanations. The authors provide a comprehensive survey of Shapley value feature attribution algorithms by disentangling and clarifying the fundamental challenges underlying their computation.
The shaky foundations of large language models and foundation models for electronic health records

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.
Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

This study tenders a new nature-inspired metaheuristic algorithm (MA) based on the behavior of the Genghis Khan shark (GKS), called GKS optimizer (GKSO), which is used for numerical optimization and engineering design. The inspiration for GKSO comes from the predation and survival behavior of GKS, and the entire optimization process is achieved by simulating four different activities of GKS, including hunting (exploration), movement (exploitation), foraging (switch from exploration to exploitation), and self-protection mechanism. These operators are mimicked using various mathematical models to efficiently perform optimization tasks of agents in different regions of the search space. In an effort to validate this method's viability and superiority, an in-depth analysis of the proposed GKSO is carried out from both qualitative and quantitative perspectives. Qualitative analysis verifies that GKSO has good exploration and exploitation (ENE) capability. Simultaneously, GKSO is quantitatively analyzed with eight existing fish optimization algorithms and the other nine well-known MAs on CEC2019 and CEC2022, respectively. Among them, a series of experimental scenarios are conducted to validate the applicability and robustness of GKSO by exploring its performance for CEC2022 at different dimensions and maximum fitness evaluation quantity. Statistical results indicate that GKSO has a strong advantage in the competition between two different types of algorithms. Furthermore, five different kinds of real-world constrained optimization problems (OPs) in CEC2020 benchmark constrained optimization functions, including 50 engineering case suites, are selected to evaluate GKSO's performance and the other seven optimizers, further validating GKSO's extensive usefulness and validity in solving practical complex problems.
The educational and pedagogical intervention in scientific research

Educational intervention requires professionals to act responsibly by employing methods and procedures that ensure appropriate outcomes. In addition, they must submit their interventions to an analysis based on scientific research for accurate evaluation. It is important to note that educational intervention and pedagogical intervention are not identical concepts, and it is necessary to establish the differences between the two. Educational intervention implies respecting the agency of the learner, which means that the educator's actions should result in a response on the part of the learner that does not necessarily have to be intentionally educational in nature, but may be a genuine outcome. On the other hand, pedagogical intervention refers to intentional actions that are carried out in the context of the educational task, with the purpose of achieving objectives and using resources supported by sound knowledge about education and the functioning of the educational system. This paper addresses the uniqueness of educational intervention and highlights its strength and validity when based on scientific research.
Fractional Wavelet Scattering Network and Applications

Objective: The present study introduces a fractional wavelet scattering network (FrScatNet), which is a generalized translation invariant version of the classical wavelet scattering network (ScatNet). Methods: In our approach, the FrScatNet is constructed based on the fractional wavelet transform (FRWT). The fractional scattering coefficients are iteratively computed using FRWTs and modulus operators. The feature vectors constructed by fractional scattering coefficients are usually used for signal classification. In this work, an application example of FrScatNet is provided in order to assess its performance on pathological images. Firstly, the FrScatNet extracts feature vectors from patches of the original histological images under different orders. Then we classify those patches into target (benign or malignant) and background groups. And the FrScatNet property is analyzed by comparing error rates computed from different fractional orders respectively. Based on the above pathological image classification, a gland segmentation algorithm is proposed by combining the boundary information and the gland location. Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is significantly improved in fractional scattering domain. We also compare the FrScatNet based gland segmentation method with those proposed in the 2015 MICCAI Gland Segmentation Challenge and our method achieves comparable results. Conclusion: The FrScatNet is shown to achieve accurate and robust results. More stable and discriminative fractional scattering coefficients are obtained by the FrScatNet in this work. Significance: The added fractional order parameter is able to analyze the image in the fractional scattering domain.
Low-Resource Syntactic Transfer with Unsupervised Source Reordering

Mohammad Sadegh Rasooli, Michael Collins. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Complete analysis of hyperentangled Bell states assisted with auxiliary hyperentanglement

We present a simple protocol for complete analysis of 16 hyperentangled Bell states of two-photon system in the polarization and the first longitudinal momentum degrees of freedom (DOFs). This complete analysis protocol is accomplished with the auxiliary hyperentangled Bell state in the frequency and the second longitudinal momentum DOFs utilizing the experimentally available optical elements including linear optical elements which manipulate the polarizations and the longitudinal momentums and the optical devices which manipulate frequencies of photons. This complete analysis protocol allows the transmission of log216=4 bits of classical information via quantum hyperdense coding scheme, which is the upper bound of the transmission capacity of the quantum hyperdense coding scheme based on 16 orthogonal hyperentangled Bell states. This complete analysis protocol has a potential to be experimentally realized and is useful for high-capacity quantum communication based on hyperentangled states.
Human few-shot learning of compositional instructions

People learn in fast and flexible ways that have not been emulated by machines. Once a person learns a new verb he or she can effortlessly understand how to dax twice, walk and dax, or dax vigorously. There have been striking recent improvements in machine learning for natural language processing, yet the best algorithms require vast amounts of experience and struggle to generalize new concepts in compositional ways. To better understand these distinctively human abilities, we study the compositional skills of people through language-like instruction learning tasks. Our results show that people can learn and use novel functional concepts from very few examples (few-shot learning), successfully applying familiar functions to novel inputs. People can also compose concepts in complex ways that go beyond the provided demonstrations. Two additional experiments examined the assumptions and inductive biases that people make when solving these tasks, revealing three biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We discuss the implications for cognitive modeling and the potential for building machines with more human-like language learning capabilities.
DoT-GNN

Most person re-identification (ReID) approaches focus on retrieving a person-of-interest from a database of collected individual images. In addition to the individual ReID task, matching a group of persons across different camera views also plays an important role in surveillance applications. This kind of Group Re-identification (GReID) task is very challenging since we face the obstacles not only from the appearance changes of individuals, but also from the group layout and membership changes. In order to obtain robust representation for the group image, we design a Domain-Transferred Graph Neural Network (DoT-GNN) method. The merits are three aspects: 1) Transferred Style. Due to the lack of training samples, we transfer the labeled ReID dataset to the G-ReID dataset style, and feed the transferred samples to the deep learning model. Taking the superiority of deep learning models, we achieve a discriminative individual feature model. 2) Graph Generation. We treat a group as a graph, where each node denotes the individual feature and each edge represents the relation of a couple of individuals. We propose a graph generation strategy to create sufficient graph samples. 3) Graph Neural Network. Employing the generated graph samples, we train the GNN so as to acquire graph features which are robust to large graph variations. The key to the success of DoT-GNN is that the transferred graph addresses the challenge of the appearance change, while the graph representation in GNN overcomes the challenge of the layout and membership change. Extensive experimental results demonstrate the effectiveness of our approach, outperforming the state-of-the-art method by 1.8% CMC-1 on Road Group dataset and 6.0% CMC-1 on DukeMCMT dataset respectively.
Thieves on Sesame Street! Model Extraction of BERT-based APIs

We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model. Assuming that both the adversary and victim model fine-tune a large pretrained language model such as BERT (Devlin et al., 2019), we show that the adversary does not need any real training data to successfully mount the attack. In fact, the attacker need not even use grammatical or semantically meaningful queries: we show that random sequences of words coupled with task-specific heuristics form effective queries for model extraction on a diverse set of NLP tasks including natural language inference and question answering. Our work thus highlights an exploit only made feasible by the shift towards transfer learning methods within the NLP community: for a query budget of a few hundred dollars, an attacker can extract a model that performs only slightly worse than the victim model. Finally, we study two defense strategies against model extraction—membership classification and API watermarking—which while successful against some adversaries can also be circumvented by more clever ones.
Deterministically fabricated spectrally-tunable quantum dot based single-photon source

Spectrally-tunable quantum light sources are key elements for the realization of long-distance quantum communication. A deterministically fabricated single-photon source with a photon extraction efficiency of η =(20 ± 2) %, a maximum tuning range of Δ E = 2.5 meV and a minimum g (2) ( τ = 0) = 0.03 ± 0.02 is presented. The device consists of a single pre-selected quantum dot (QD) monolithically integrated into a microlens that is bonded onto a piezoelectric actuator via gold thermocompression bonding. Here, a thin gold layer simultaneously provides strain transfer and acts as a backside mirror for the QD-microlens to maximize the photon extraction efficiency. The QD-microlens structure is patterned via 3D in-situ electron-beam lithography (EBL), which allows us to pre-select and integrate suitable QDs based on their emission intensity and energy with a spectral accuracy of 1 meV for the final device. Together with strain fine-tuning, this enables the scalable realization of single-photon sources with identical emission energy. Moreover, we show that the emission energy of the source can be stabilized to µeV accuracy by closed-loop optical feedback. Thus, the combination of deterministic fabrication, spectral-tunability and high broadband photon-extraction efficiency makes the QD-microlens single-photon source an interesting building block for the realization of quantum communication networks.
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles

The inaccuracy of neural network models on inputs that do not stem from the distribution underlying the training data is problematic and at times unrecognized. Uncertainty estimates of model predictions are often based on the variation in predictions produced by a diverse ensemble of models applied to the same input. Here we describe Maximize Overall Diversity (MOD), an approach to improve ensemble-based uncertainty estimates by encouraging larger overall diversity in ensemble predictions across all possible inputs. We apply MOD to regression tasks including 38 Protein-DNA binding datasets, 9 UCI datasets, and the IMDB-Wiki image dataset. We also explore variants that utilize adversarial training techniques and data density estimation. For out-of-distribution test examples, MOD significantly improves predictive performance and uncertainty calibration without sacrificing performance on test data drawn from same distribution as the training data. We also find that in Bayesian optimization tasks, the performance of UCB acquisition is improved via MOD uncertainty estimates.
PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Cache conflicts due to deterministic memory-tocache mapping have long been exploited to leak sensitive information such as secret keys.While randomized mapping is fully investigated for L1 caches, it still remains unresolved about how to secure a much larger last-level cache (LLC).Recent solutions periodically change the mapping strategy to disrupt the crafting of conflicted addresses, which is a critical attack procedure to exploit cache conflicts.Remapping, however, increases both miss rate and access latency.We present PhantomCache for securing an LLC with remapping-free randomized mapping.We propose a localized randomization technique to bound randomized mapping of a memory address within only a limited number of cache sets.The small randomization space offers fast set search over an LLC in a memory access.The intrinsic randomness still suffices to obfuscate conflicts and disrupt efficient exploitation of conflicted addresses.We evaluate PhantomCache against an attacker exploring the state-of-the-art attack with linear-complexity.To secure an 8-bank 16 MB 16-way LLC, PhantomCache confines randomization space of an address within 8 sets and brings only 1.20% performance degradation on individual benchmarks, 0.50% performance degradation on mixed workloads, and 0.50% storage overhead per cache line, which are 2x and 9x more efficient than the state-of-the-art solutions.Moreover, PhantomCache is solely an architectural solution and requires no software change.
Sequence-Dropout Block for Reducing Overfitting Problem in Image Classification

Overfitting is a common problem for computer vision applications It is a problem that when training convolution neural networks and is caused by lack of training data or network complexity. The novel sequence-dropout (SD) method is proposed in this paper to alleviate the problem of overfitting when training networks. The SD method works by dropping out units (channels of feature) from the network in a sequence, replacing the traditional operation of random omitting. Sophisticated aggregation strategies are used to obtain the global information of feature channels, and channel-wise weights are produced by gating mechanism. The SD method then selectively drops out the feature channels according to the channel-wise weights that represent the importance degree of each channel. The proposed SD block can be plugged into state-of-the-art backbone CNN models such as VGGNet and ResNet. The SD block is then evaluated on these models, demonstrating consistent performance gains over the baseline model on widely-used benchmark image classification datasets including MNIST, CIFAR-10, CIFAR-100, and ImageNet2012. Experimental results demonstrate that the superior performance of the SD block compared to other modern methods.
Creating Bioethics Distance Learning Through Virtual Reality

Bioethics education is a central element in the biotechnology curriculum. Re-imagining distance learning, virtual reality (VR) is taking student involvement to the next level of interaction, offering a real classroom experience and a new way to gain ethical reasoning skills. Here, we explore a new paradigm for bioethics education that involves VR.