Reviewerly/exHarmony huggingface dataset:
Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex

Abstract Discrete neural states are associated with reaching movements across the frontoparietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuo-motor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatio-temporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example in brain-machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.
Libraries in the Age of Intelligent Information: AI-Driven Solutions

This study explores the paradigm change that artificial intelligence (AI) has brought about in the context of libraries in the age of intelligent information. A quantitative survey was carried out, and a methodical description of the study's procedure is provided. The research sampling strategy, data gathering, data collection tool, and statistical analysis are all included in this quantitative approach. Additionally, a random sampling procedure was used to choose the study sample. Similar to this, questionnaires were employed as response instruments to collect data in both physical and digital formats. Findings from the investigation showed that 45% of institutions are private nature and 50% institutions shows the status public. 1-10 years’ experience were 62% respondents, 11-20 years’ experience were 31% and only 8% of respondents were 21 years above experience. The study emphasizes how important it is to create a welcoming and open library ecosystem where artificial intelligence (AI) enhances operational effectiveness while protecting user privacy and pleasure. All things considered, this thorough analysis highlights the many ways that artificial intelligence (AI) might transform libraries and meet the many demands of their patrons. Looking forward, the article discusses the promising future of AI in libraries, envisioning AI-powered chatbots, predictive analytics, and virtual helpers as integral components of an AI-driven library landscape.
Deep Learning Methods for Diagnosing Thyroid Cancer

Abstract One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network

Abstract Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using Transfer Entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.
Maximization of rf-signal recovery in quantum microwave photonics systems

Quantum microwave photonics (QMWP) technology, which harnesses energy-time-entangled biphotons as optical carriers and employs time-correlated single-photon detection for high-speed rf-signal recovery, has demonstrated remarkable potential in achieving nonlocal rf-signal coding and efficient rf-signal recovery from significant dispersion interference. In this paper, we expound on the principle of maximizing the recovered rf signals in QMWP systems. Through theoretical analysis and experimental validation, we establish a precise relationship between the recovered rf signal's magnitude, the input rf signal's frequency, involved dispersions, and the width of the coincidence selection window. This enables us to determine the optimal width for the coincidence selection window and the corresponding magnitude of the maximized rf signal given the dispersion. These findings offer valuable guidance for improving QMWP systems, thus expanding their potential applications.
FPGA-based multipliers for elliptic curve cryptography

This paper presents an approach to build large-integer multipliers for Elliptic Curve Cryptography (ECC). Compared to the traditional divide and conquer method, our method introduces the 𝑛-term Karatsuba-like algorithm and adopts a cascaded structure for integer multiplications, which simplifies the complexity of the whole structure and contributes to a fine-grained multiplier design. Target at the required width of random elliptic curves over prime fields, we implement the design on virtex-7 FPGA and utilize internal DSPs as the fundamental multiplier resources. The synthesis results are compared quantitatively in terms of designs with different parameters and demonstrate the flexibility of our method.
Requirements modeling for weather modification services based on problem frames

This paper conducts an in-depth requirement analysis of artificial rainmaking operations using the ontology-guided problem framework method, constructing a clear and organized structure to systematically reveal the complexity and multi-level nature of artificial rainmaking operations. In the process of requirement modeling, we precisely define key aspects such as business goals, technical constraints, and environmental impacts, contributing to a more comprehensive understanding of the nature of the business. This ontology-guided problem framework method, based on ontology guidance, provides a unique and practical approach for artificial weather modification operations, offering systematic and actionable guidance for relevant decision-making and implementation. The aim of this paper is to provide more precise guidance for the planning and implementation of artificial rainmaking projects, thereby promoting the further development and application of artificial weather modification technology.
Expandable residual attention-based high-performance embedded gesture recognition

In embedded human-computer interaction systems, the development of high-performance gesture recognition technology is crucial due to its demand for low power consumption and efficient processing. Addressing the challenge of highprecision gesture recognition in complex backgrounds, a high-performance embedded gesture recognition method based on the Expandable Residual Attention mechanism is proposed. This method enhances the capability of extracting differentscale gesture features by introducing the Expandable Residual Attention mechanism into YOLOv7. Additionally, to address the characteristics of high degrees of freedom and self-occlusion in hand gestures, SoftNMS is introduced with a penalty term to effectively reduce the probability of target omissions. Finally, the gesture recognition model is compressed and accelerated with TensorRT. Experimental results on the Jochen Triesch Static Hand Posture Database demonstrate that the proposed method significantly improves gesture recognition accuracy while maintaining high inference efficiency.
Rules as code and the rule of law: ensuring effective judicial review of administration by software

This paper considers the possible benefits and substantial risks of ‘Rules as Code’, the parallel drafting of legislation and codification in software, which has been the subject of attention from policy-makers and pilot studies in some jurisdictions. It highlights the long history of these approaches, and the challenges of ossification, mis-translation of rules, and separation of powers problems. It also examines in the detail the Australian Pintarich case, which demonstrates the inadequacy of conventional judicial review of automated decision-making. It outlines some possible solutions to these issues — two ‘internal’ to development processes (greater transparency, and literate pair programming) and two ‘external’ (expanding the capacity of judicial review to look beyond a specific citizen/state interaction and consider the design and development of the controlling software system, and greater cross-disciplinary awareness by lawyers).
Temporal analysis of hospitalizations for Diabetes Mellitus in Brazil and its relationship with COVID-19: ecological study

Diabetes mellitus (DM) is a condition resulting from changes in cartilage or the effect of insulin, resulting in chronic hyperglycemia. In diabetic patients, the presence of COVID-19 increases the rates of serious complications and there is a greater risk of mortality. To analyze the phenotypic characteristics of hospitalizations and mortality rates due to DM in Brazil through epidemiological data and their relationship with COVID-19. This is an observational, descriptive, cross-sectional, quantitative and retrospective study; carried out with analysis of secondary data on hospitalizations for DM, through the Information Technology Department of the Unified Health System (DataSUS) in the period from 2018 to 2022 in Brazil. Variables of year of service, region, race/color, age group and sex were considered. In the present analysis, there is a greater MT in the Southeast and Northeast regions of Brazil. In terms of gender distribution, the highest prevalence is in males (51.05%), but with lower MT than females (2.69 x 2.84), with the population aged between 50 and over. 80 years old with higher percentages of international and TM. Finally, it is clear that the Brown race required more hospitalizations (41.68%), while the Black race was associated with the highest mortality rates. In the present study, it is possible to analyze that the phenotypic characteristics of diabetic patients influence the mortality prognosis, providing the basis for further research with the aim of analyzing the mechanisms that lead to the epidemiological aspects with greater prevalence of TM.
Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.
Time-based sampling: Theory and an application to video reconstruction from events

Traditional signal processing is based on the idea that an analogue waveform should be converted in digital form by recording its amplitude information at specific time instants. Nearly all data acquisition, processing and communication methods have progressed by relying on this fundamental sampling paradigm. Interestingly, we know that the brain operates differently and represents signals using networks of spiking neurons where the timing of the spikes encodes the signal's information. This form of processing by spikes is more efficient and is inspiring a new generation of event-based audio-visual sensing and processing architectures. In this talk, we investigate time encoding as an alternative method to classical sampling, and address the problem of reconstructing classes of sparse non-bandlimited signals from time-based samples. We consider a sampling mechanism based on first filtering the input, before obtaining the timing information using a time encoding machine. Leveraging specific properties of these filters, we derive sufficient conditions and propose novel algorithms for perfect reconstruction of classes of sparse signals. We then extend our analysis to multi-dimensional signals and introduce a deep neural networks for the reconstruction of intensity videos from events generated by event-driven cameras. The network is obtained by embedding prior-models in its architecture. We show that this approach leads to simple and yet effective neural networks.
Enhancing sentence semantic matching with isotropic batch normalization and generalized pooling operator

Sentence semantic matching plays a crucial role in various natural language processing (NLP) tasks such as question answering, information retrieval, and text classification. Prior research has made significant contributions to sentence semantic matching, but there is still room for improvement, especially in handling subtle semantic representations. This paper introduces a new approach to sentence semantic matching that integrates Isotropic Batch Normalization and Generalized Pooling Operator, two advanced neural network architectures. By combining these techniques, we aim to enhance the accuracy and efficiency of semantic matching and address the challenges of capturing subtle semantic representations. We compare our approach to existing state-of-the-art models and demonstrate its effectiveness through comprehensive experiments on benchmark datasets.
Media Criminology – The role of the media and its influence on contemporary society

This study explores the role of media criminology and its influence on contemporary society, emphasizing the portrayal of crime and penal justice by the media. It aims to clarify the significant consequences of media representations on public perception of crime and justice. Our research methodology is grounded on an extensive review of significant literature, avoiding direct quotes but emphasizing the importance of understanding media's role in shaping societal perspectives. The study reveals that media, through its depictions of crime, significantly influences public perception and understanding of criminal justice. Moreover, we found that the evolution of technology, specifically social media, plays a substantial role in media criminology. We conclude by asserting the crucial necessity for a critical understanding and questioning of media representations of crime in our contemporary society.
Weighted median autoregressive graph filters

The graph filter can extract the desired features from the graph signal and filter out the noise signal. Most of the graph filters proposed in the literature are linear. Autoregressive moving average (ARMA) filter is a polynomial filter. Compared to finite-impulse response (FIR) graph filters, ARMA graph filters are robust to changes in the signal and/or graph, but are still linear. In this work, we propose a weighted median autoregressive graph filter (WMAF) based on a first-order ARMA graph filter. The proposed filter is a combination of weighted median filter in the traditional signal processing field and median autoregressive filters (MAF), and can be implemented in a distributed way. Compared with linear filter and MAF, the proposed WMAF filter has better filtering effect on pulse noise. In the denoising application of real sensor network data set, the filtered signal has a better signal-to-noise ratio.
Job resource prediction and completion status prediction on large-scale computing platforms

Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name.
Weighted and Mumford-Shah losses for weakly supervised semantic segmentation

In weakly supervised semantic segmentation (WSSS) tasks, the noise in pseudo labels poses a significant challenge to the training of segmentation networks. However, the widely used cross-entropy loss, which only considers individual pixel information in the images, is insufficient to address this issue. In order to effectively train segmentation networks using noisy labels, we propose a Weakly Supervised Semantic Segmentation method (WMS) based on weighted loss and Mumford-Shah loss. Firstly, we introduce a weighted loss with confidence weights for pseudo labels. This loss assigns weights to each pseudo label by incorporating a confidence indicator, enhancing the network's ability to resist noise interference. Additionally, we propose a Mumford-Shah (MS) loss based on variational segmentation. By leveraging the similarity between pixels in the original image, this loss introduces additional noise-free self-supervised information to assist in the training of the segmentation network, further suppressing the interference caused by noisy labels. Extensive experiments on the PASCAL VOC 2012 dataset demonstrate that the proposed method significantly improves WSSS performance.
Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

Abstract Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent \;$z$\; and spatial correlation length correlation exponent \;$\nu_{\perp}$\ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.
Using high-precision travel time extraction to detect time deviation of seismic instruments

Abstract The method of seismic ambient noise cross-correlations (NCFs) has been demonstrated to be applicable for time offset measurement, especially in evaluating instrument clocks. Continuous recording of seismic ambient noise data makes it possible to analyze the performance of seismic instruments online. However, long-term cross-correlation and stacking calculations are required to obtain accurate travel time from ambient noise, which greatly reduces the time resolution of instrument performance detection. Therefore, we propose a travel time extraction method based on time–frequency analysis, which could obtain the travel time accurately even though the NCF has a low signal-to-noise ratio (SNR), and it is applied to the measurement of clock offsets in seismic instruments. The method combines S Transform and dictionary learning to improve the SNR of NCFs, and uses a peak extraction algorithm based on short-time Fourier transform to obtain accurate travel times. Additionally, the travel time drift of the station pair can be obtained by calculating the travel time difference between causal and acausal parts of NCFs. Using the data from ambient noise observations and the real data with time errors to verify that the proposed method can obtain accurate travel time for NCF with SNR lower than 6 and it can pick up a time offset as low as one sampling point, the detectable change is 0.046% of travel time, which is crucial for detecting weak changes in the performance of seismic instruments.