Reviewerly/exHarmony huggingface dataset:
Multisets and Distributions, in Drawing and Learning

Multisets are ‘sets’ in which elements may occur multiple times. Discrete probability distributions capture states in which elements may occur with probabilities that add up to one. This paper describes how the interaction between multisets and distributions lies at the heart of some basic constructions in probability theory, especially in distributions arising from drawing from an urn with multiple balls and in learning distributions from multiple occurrences of data. Drawing multiple balls from an urn is described uniformly in terms of Kleisli iteration for a monad, covering the four standard distinctions of ordered/unordered draws, with/without replacement. In probabilistic learning the paper distinguishes two forms of likelihood, based on also on iteration, with corresponding forms of learning. Both of these forms occur in the literature, but they are not clearly distinguished, even though they lead to different outcomes.
UcUNet: A lightweight and precise medical image segmentation network based on efficient large kernel U-shaped convolutional module design

In recent years, precise medical image segmentation methods based on the encoder–decoder structure have attracted much attention, but there are still some limitations. They mainly manifest in two aspects: one is that the network structure becomes increasingly complex in pursuit of segmentation accuracy, and the other is the insufficient multiscale information fusion ability between the encoder and decoder. To address these issues, we designed a novel lightweight precise medical image segmentation network called UcUNet, which has not only a very large receptive field and multiscale information fusion ability but also a low parameter count. Specifically, we first designed an efficient U-shaped convolution module that can increase the network depth with fewer parameters and effectively filter out invalid features while fusing shallow and deep features. Furthermore, we ingeniously introduced large-kernel convolution into this module, which significantly improved the network’s receptive field with very few parameters, providing new ideas for the use of large-kernel convolution in lightweight models. In addition, we designed a self-adjusting multiscale feature fusion module based on large-kernel convolution to replace the original simple skip connections, effectively enhancing the network’s multiscale information extraction and fusion ability. Finally, we conducted experiments on multiple datasets, and the results showed that our method can achieve precise medical image segmentation with a low parameter count.
Selective and collaborative influence function for efficient recommendation unlearning

Recent regulations concerning the Right to be Forgotten have greatly influenced the operation of recommender systems, because users now have the right to withdraw their private data. Besides simply deleting the target data in the database, unlearning the associated data lineage e.g., the learned personal features and preferences in the model, is also necessary for data withdrawal. Existing unlearning methods are mainly devised for generalized machine learning models in classification tasks. In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i.e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items. To tackle the above issues, we propose a highly efficient recommendation unlearning method based on Selective and Collaborative Influence Function (SCIF). Our proposed method can (i) avoid any kind of retraining which is computationally prohibitive for large-scale systems, (ii) further enhance efficiency by selectively updating user embedding and (iii) preserve the collaboration across the remaining users and items. Furthermore, in order to evaluate the unlearning completeness, we define a Membership Inference Oracle (MIO) that verifies whether the unlearned data points were part of the model's training set, thereby determining if a data point was completely unlearned. Extensive experiments on two benchmark datasets demonstrate that our proposed method can not only greatly enhance unlearning efficiency, but also achieve adequate unlearning completeness. More importantly, our proposed method outperforms the State-Of-The-Art (SOTA) unlearning method regarding comprehensive recommendation metrics.
Credit market development and agricultural production in selected African countries

This study aimed to examine the long and short-run relationships between credit market development and agricultural production using the Autoregressive Distributed Lags (ARDL) Bounds test for cointegration, as well as the direction of causality by using the Granger causality test. The results of the ARDL Bounds test revealed that institutional credit development had a significant long-run effect on agricultural production in all countries under examination, except for Tunisia; that is: Benin, Kenya, and Nigeria. In the short run, credit market development had a significant and positive effect on agricultural production in Nigeria. By contrast, the effect of credit market development on agricultural production was negative in the short-run in Benin, Kenya, and Tunisia. The result of granger causality test revealed the existence of significant bi-directional causal links between institutional credit development and agricultural production in Benin, Kenya and Nigeria, no significant interdependence was found between the two variables in Tunisia. Capital formation had a significant and positive effect on agricultural production in the long-run and short-run in all countries. Climate change was negatively associated with agricultural production in all countries except for Nigeria. The exchange rates and real interest rates had a negative effect on agricultural production in the long-run and short-run in all countries.
Competing imaginaries: Crypto‐utopianism and the material forces of Bitcoin mining

Drawing on Marx's theory of history, this article argues that the competition and capital accumulation inherent in the production of Bitcoin (i.e. ‘mining’) are at odds with the narrative discourses that position Bitcoin as a revolutionary technology capable of subverting traditional power structures. Through an analysis of the evolution of Bitcoin mining, the article demonstrates how the material conditions of its production have shifted over time, leading to the concentration of mining power among a few large corporate entities and a concomitant erosion of the decentralized ethos that underpinned the early Bitcoin community. The article also argues that this shift is not simply a result of the ‘natural’ evolution of the technology, but also the outcome of specific social and economic forces that encourage the accumulation of capital over Bitcoin's democratic and decentralized potentialities. Overall, the article suggests that the narrative discourses surrounding Bitcoin need to be understood in relation to the material forces that shape its production and circulation, and that a more nuanced analysis of the interplay between material and discursive factors is necessary to fully grasp the dynamics of the cryptocurrency ecosystem.
The philosophy of the metaverse

Abstract How might we philosophize about the metaverse? It is traditionally held that the four main branches of philosophy are metaphysics, epistemology, axiology, and logic. In this article, I shall demonstrate how virtual walt-fictionalism, a particular version of virtual irrealism, is able to offer a straightforward, internally consistent, and powerful response about the metaphysics, epistemology, and axiology (ethics) of the metaverse. I will first characterize the metaverse in terms of a reality-virtuality (RV) continuum and distinguish between virtual realism and virtual irrealism, before elaborating on the explanatory power and potential of virtual walt-fictionalism relative to the metaphysics, epistemology, and axiology (ethics) of the metaverse.
Classical splitting of parametrized quantum circuits

Abstract Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms. They can be caused by multiple factors such as the expressivity of the ansatz, excessive entanglement, the locality of observables under consideration, or even hardware noise. We propose classical splitting of parametric ansatz circuits to avoid barren plateaus. Classical splitting is realized by subdividing an N qubit ansatz into multiple ansätze that consist of $$\mathcal {O}(\log N)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mo>log</mml:mo> <mml:mi>N</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> qubits. We show that such an approach allows for avoiding barren plateaus and carry out numerical experiments, and perform binary classification on classical and quantum datasets. Moreover, we propose an extension of the ansatz that is compatible with variational quantum simulations. Finally, we discuss a speed-up for gradient-based optimization and hardware implementation, robustness against noise and parallelization, making classical splitting an ideal tool for noisy intermediate scale quantum (NISQ) applications.
Perceiving local relative motion and global correlations for weakly supervised group activity recognition

This paper presents a weakly supervised approach for group activity recognition by exploiting the local relative motion and global correlations among entities. Most existing approaches of group recognition characterize motions of group activities based on the coordinates of individuals or feature maps without excluding the camera motion, which is a combination of local relative motion and camera motion. To address this problem, we utilize a simple but effective Local Relative Motion Module (LRMM): a 3D-CNN-based network to exploit the local movement. We further employ a Global Correlation Module (GCM) to establish relationships among different feature patches for capturing the entire scene. We have evaluated the proposed method on sports and group activity video. The method has achieved state-of-the-art performance on three challenging datasets for weakly supervised group activity recognition. The method has also outperformed some approaches trained with much stronger supervision in the comparative evaluation.
Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection

The rapid spread of media content synthesis technology and the potentially damaging impact of audio and video deepfakes on people’s lives have raised the need to implement systems able to detect these forgeries automatically. In this work we present a novel approach for synthetic speech detection, exploiting the combination of two high-level semantic properties of the human voice. On one side, we focus on speaker identity cues and represent them as speaker embeddings extracted using a state-of-the-art method for the automatic speaker verification task. On the other side, voice prosody, intended as variations in rhythm, pitch or accent in speech, is extracted through a specialized encoder. We show that the combination of these two embeddings fed to a supervised binary classifier allows the detection of deepfake speech generated with both Text-to-Speech and Voice Conversion techniques. Our results show improvements over the considered baselines, good generalization properties over multiple datasets and robustness to audio compression.
Vector field-based integral LOS path following and target tracking for underactuated unmanned surface vehicle

The paper focuses on developing a method that enables the unmanned surface vehicle (USV) to effectively track dynamic objects on the water surface. The integral line-of-sight (LOS) guidance law is improved by introducing the concept of vector field for curved paths and the time-varying lookahead distance with a smoothing control parameter for straight paths. These enhancements facilitate faster convergence to the desired path with reduced oscillations and allow for mitigating the impact of constant external disturbances. The research demonstrates that the proposed guidance law exhibits κ-exponential stability when converging towards a desired path composed of both straight lines and curves. The results presented in the paper demonstrate that the proposed method effectively improves the accuracy of tracking dynamic targets for unmanned surface vehicles while ensuring the safety of docking operations.
Towards U-statistics clustering inference for multiple groups

AbstractInference in clustering is paramount to uncovering inherent group structure in data. Clustering methods that assess statistical significance have recently drawn attention due to their role in identifying patterns in high-dimensional data with applications in many scientific fields. Towards developing a general framework for clustering in multiple groups, we present here a U-statistics-based approach, specially tailored for high-dimensional datasets, that clusters the data into three groups while assessing the significance of such partitions. We also consider theoretical aspects of allowing for an outlier group. Our approach stands on the U-statistics-based clustering framework of the methods in R package uclust and inherits its properties being a non-parametric method relying on very few assumptions about the data. Thus it can be applied to a wide range of datasets. Furthermore our method aims to be a statistically powerful tool to find the best partitions of the data into three groups when that particular structure is present. To do so, we first propose an extension of the test U-statistic and develop its asymptotic theory. Additionally we propose a ternary non-nested significance clustering method. Our approach is tested through multiple simulations and is shown to be comparable or have more statistical power to competing alternatives in all scenarios considered. An application to image recognition data showcases our method.Keywords: High-dimensional datamodel free inferencesignificance clustering AcknowledgmentsWe would like to thanks Pedro Fusieger for the fruitful discussions on the number of possible assignments of all n elements in three subgroups.Data availability statementR-functions containing all methods developed in this article (available in the uclust package at CRAN). Dataset used in the application and scripts available at https://github.com/marciovalk/code_uclust3_paper.git.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingDebora Zava Bello was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
GW-net: An efficient grad-CAM consistency neural network with weakening of random erasing features for semi-supervised person re-identification

Person re-identification (Re-ID) subject to loss of detailed information (sacrificing certain intricate body details) in samples caused by conventional regularization algorithms and degraded cross-domain performance due to limited generalization capacity is a challenging task. The majority of the existing research efforts address this problem either for attention regularization or cross-domain task, but neglect to explore a powerful framework to consider solving both cases simultaneously. To overcome this limitation, this paper develops an efficient semi-supervised person Re-ID network with Grad-CAM consistency regularization and weakening of random erasing features (GW-Net) to explore rich features and expect non-degraded performance in cross-domain condition. Specifically, a Grad-CAM consistency regularization (GCCR) module is designed to capture detailed information by using Grad-CAM for consistency regularization, thereby enhancing the capacity of intra-class feature mining. Secondly, a weakening of random erasing features (WREF) module is presented to reduce the impact of erased regions on texture features, thereby maintaining the performance on the general source domain while preventing performance degradation in the target domain. Thirdly, a Grad-CAM consistency regularization loss is introduced to enable our model to maintain consistency between Grad-CAM results of the unlabeled and its augmented images. Meanwhile, a feature consistency loss is reported to keep consistency between features of unlabeled samples. Finally, sufficient experiments are carried out on three representative datasets, which validate the efficacy and meliority of our presented approach over state-of-the-art methods.
Naming entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model

In the agricultural industry, there is still a need for improved systematic integration of knowledge related to citrus pests and diseases. Creating a knowledge base focused on citrus pests and diseases and developing a knowledge map would enable the formation of a system providing accurate and convenient prevention and control methods for citrus growers. However, the majority of existing data on citrus pests and diseases is unstructured, making identifying named entities from large volumes of unstructured text data particularly important for creating a knowledge map. In this paper, we propose a training model based on Bidirectional Encoder Representation from Transformers (BERT), combined with Bidirectional Long and Short Term Memory Networks (BiLSTM) and Conditional Random Field (CRF), to extract specific entity categories from unstructured data. When tested on a created dataset, the model achieved an accuracy rate of 0.9423 and an f1 value of 0.8048. The results demonstrate that the proposed method can be applied to extract specific entity categories from text data related to citrus pests and diseases, laying a solid foundation for the subsequent construction of a knowledge map. This paper introduces a method for named entity recognition in the field of citrus pests and diseases, which could serve as a reference for constructing knowledge maps in other fields and improving the utilization of domain knowledge.
The Lumigraph

This paper discusses a new method for capturing the complete appearance of bothsynthetic and real world objects and scenes,representing this information, and then using this representation to render images of the object from new camera positions. Unlike the shape capture process traditionally used in computer vision and the rendering process traditionally used in computer graphics, our approach does not rely on geometric representations. Instead we sample and reconstruct a 4D function. which we call a Lumigraph. The Lumigraph is a subset o f the complete plenoptic fu nction that describes the flow of light at all positions in all directions. With the Lumigraph. new images of the object can be generated very quick]y,independentof the geometric or illumination complexity of the scene or object. The paper discusses a complete working system including the capture of sainples. the construction of the Lumigraph, and thesubsequent rendering of images from this new representation.
Effective Feature Engineering and Classification of Breast Cancer Diagnosis: A Comparative Study

Breast cancer is among the most common cancers found in women, causing cancer-related deaths and making it a severe public health issue. Early prediction of breast cancer can increase the chances of survival and promote early medical treatment. Moreover, the accurate classification of benign cases can prevent cancer patients from undergoing unnecessary treatments. Therefore, the accurate and early diagnosis of breast cancer and the classification into benign or malignant classes are much-needed research topics. This paper presents an effective feature engineering method to extract and modify features from data and the effects on different classifiers using the Wisconsin Breast Cancer Diagnosis Dataset. We then use the feature to compare six popular machine-learning models for classification. The models compared were Logistic Regression, Random Forest, Decision Tree, K-Neighbors, Multi-Layer Perception (MLP), and XGBoost. The results showed that the Decision Tree model, when applied to the proposed feature engineering, was the best performing, achieving an average accuracy of 98.64%.
Composed Image Retrieval via Cross Relation Network With Hierarchical Aggregation Transformer

Composing Text and Image to Image Retrieval (CTI-IR) aims at finding the target image, which matches the query image visually along with the query text semantically. However, existing works ignore the fact that the reference text usually serves multiple functions, e.g., modification and auxiliary. To address this issue, we put forth a unified solution, namely Hierarchical Aggregation Transformer incorporated with Cross Relation Network (CRN). CRN unifies modification and relevance manner in a single framework. This configuration shows broader applicability, enabling us to model both modification and auxiliary text or their combination in triplet relationships simultaneously. Specifically, CRN includes: 1) Cross Relation Network comprehensively captures the relationships of various composed retrieval scenarios caused by two different query text types, allowing a unified retrieval model to designate adaptive combination strategies for flexible applicability; 2) Hierarchical Aggregation Transformer aggregates top-down features with Multi-layer Perceptron (MLP) to overcome the limitations of edge information loss in a window-based multi-stage Transformer. Extensive experiments demonstrate the superiority of the proposed CRN over all three fashion-domain datasets. Code is available at github.com/yan9qu/crn.
Class Incremental Robotic Pick-and-Place via Incremental Few-Shot Object Detection

We introduce a new task, called Class Incremental Robotic Pick-and-Place (CIRPAP), which calls for the capacity to learn to pick and place new categories of objects while retaining the skill of dealing with the previously learned ones. CIRPAP faces three challenges: catastrophic forgetting, few-shot learning, and robust picking in cluttered environments. To address the challenges of catastrophic forgetting and few-shot learning, we propose a novel CIRPAP framework that is built on Incremental Few-Shot Object Detection (iFSD). Specifically, with fixed pre-trained Transformer-like object detection models, we only fine-tune the additional adapter modules, which is called adapter-tuning. To address the challenge of robust picking in cluttered environments, we also utilize multiview fusion to integrate object detection and grasp prediction results. As for iFSD evaluation, experiments show that our adapter-tuning-based approach outperforms state-of-the-art methods on COCO and our dataset. As for full CIRPAP system evaluation, experimental results on a real robotic platform demonstrate the effectiveness of our proposed framework.