Reviewerly/exHarmony huggingface dataset:
Quadratic Speedup for Spatial Search by Continuous-Time Quantum Walk

Continuous-time quantum walks provide a natural framework to tackle the fundamental problem of finding a node among a set of marked nodes in a graph, known as spatial search. Whether spatial search by continuous-time quantum walk provides a quadratic advantage over classical random walks has been an outstanding problem. Thus far, this advantage is obtained only for specific graphs or when a single node of the underlying graph is marked. In this Letter, we provide a new continuous-time quantum walk search algorithm that completely resolves this: our algorithm can find a marked node in any graph with any number of marked nodes, in a time that is quadratically faster than classical random walks. The overall algorithm is quite simple, requiring time evolution of the quantum walk Hamiltonian followed by a projective measurement. A key component of our algorithm is a purely analogue procedure to perform operations on a state of the form e^{-tH^{2}}|ψ⟩, which, for a given Hamiltonian H, only requires evolving H for time scaling as sqrt[t]. This allows us to quadratically fast-forward the dynamics of a continuous-time classical random walk. The applications of our Letter thus go beyond the realm of quantum walks and can lead to new analog quantum algorithms for preparing ground states of Hamiltonians or solving optimization problems.
Smart Technology, Digitalized Education Model and Young Vulnerable Brains in India: A Current Situational Analysis

It's a well-known fact that technology has become ingrained in daily life.Technology is so entrenched in every aspect of life that it's impossible to imagine our life without technology.If you take away the phone from a person he feels as if he is living in a stone age.Absence of technology makes us feel handicapped.Technology in education is a revolution enabling delivery of critical resources possible for millions of students at the same time.Online classes or webinars had become a norm these days, especially after the Covid pandemic when physical assembly was not possible and students had to study at home aided by electronic tools like Zoom or Teams to attend virtual classes.It became essential to provide portable electronic devices like laptops, smart phones, or tablets to children to participate in online classes or assignments or refer to study material.But this blessing turns out to be a curse when parents find their children addicted to online games or chatting with strangers on social media or browsing inappropriate or age-restricted content.Excessive use of technology causes numerous mental, psychological, and behavioral issues in children.This research paper has analyzed the positive and negative impact of digital technologies on children.The researcher has conducted doctrinal research based on secondary data.Data has been collected from authentic sources such as the WHO, NCPCR, ASER, India today poll, AIIMs, and the State gov.portal.Online education has set a new standard in recent years.However, removing technology from your children is impossible.While distance education, in particular, necessitates the use of technology, parents should closely monitor their children's digital footprints, screen time, browsing history, and other content or information they consume.The researchers concluded that it is critical to be mindful of children's electronic behaviors on a regular basis and to put reasonable restrictions if they are crossing the boundaries.
Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: a retrospective multi-cohort study

BackgroundDigital whole-slide images are a unique way to assess the spatial context of the cancer microenvironment. Exploring these spatial characteristics will enable us to better identify cross-level molecular markers that could deepen our understanding of cancer biology and related patient outcomes.MethodsWe proposed a graph neural network approach that emphasises spatialisation of tumour tiles towards a comprehensive evaluation of predicting cross-level molecular profiles of genetic mutations, copy number alterations, and functional protein expressions from whole-slide images. We introduced a transformation strategy that converts whole-slide image scans into graph-structured data to address the spatial heterogeneity of colon cancer. We developed and assessed the performance of the model on The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) and validated it on two external datasets (ie, The Cancer Genome Atlas rectum adenocarcinoma [TCGA-READ] and Clinical Proteomic Tumor Analysis Consortium colon adenocarcinoma [CPTAC-COAD]). We also predicted microsatellite instability and result interpretability.FindingsThe model was developed on 459 colon tumour whole-slide images from TCGA-COAD, and externally validated on 165 rectum tumour whole-slide images from TCGA-READ and 161 colon tumour whole-slide images from CPTAC-COAD. For TCGA cohorts, our method accurately predicted the molecular classes of the gene mutations (area under the curve [AUCs] from 82·54 [95% CI 77·41–87·14] to 87·08 [83·28–90·82] on TCGA-COAD, and AUCs from 70·46 [61·37–79·61] to 81·80 [72·20–89·70] on TCGA-READ), along with genes with copy number alterations (AUCs from 81·98 [73·34–89·68] to 90·55 [86·02–94·89] on TCGA-COAD, and AUCs from 62·05 [48·94–73·46] to 76·48 [64·78–86·71] on TCGA-READ), microsatellite instability (MSI) status classification (AUC 83·92 [77·41–87·59] on TCGA-COAD, and AUC 61·28 [53·28–67·93] on TCGA-READ), and protein expressions (AUCs from 85·57 [81·16–89·44] to 89·64 [86·29–93·19] on TCGA-COAD, and AUCs from 51·77 [42·53–61·83] to 59·79 [50·79–68·57] on TCGA-READ). For the CPTAC-COAD cohort, our model predicted a panel of gene mutations with AUC values from 63·74 (95% CI 52·92–75·37) to 82·90 (73·69–90·71), genes with copy number alterations with AUC values from 62·39 (51·37–73·76) to 86·08 (79·67–91·74), and MSI status prediction with AUC value of 73·15 (63·21–83·13).InterpretationWe showed that spatially connected graph models enable molecular profile predictions in colon cancer and are generalised to rectum cancer. After further validation, our method could be used to infer the prognostic value of multiscale molecular biomarkers and identify targeted therapies for patients with colon cancer.FundingThis research has been partially funded by ARO MURI 805491, NSF IIS-1793883, NSF CNS-1747778, NSF IIS 1763523, DOD-ARO ACC-W911NF, and NSF OIA-2040638 to Dimitri N Metaxas.
MCRN: A Multi-source Cross-modal Retrieval Network for remote sensing

Cross-modal remote sensing retrieval (RSCR) has an increasing importance due to the ability to quickly and flexibly retrieve valuable data from enormous remote sensing (RS) images. However, traditional RSCR methods tend to focus on the retrieval between two modalities, when the number of modalities increases, the contradiction between the increasing semantic gap and the small amount of paired data causes the model fail to learn a superior modal representation. In this paper, inspired by the visual-based modal center in RS, we construct a multi-source cross-modal retrieval network (MCRN) that manages to unify RS retrieval tasks under multiple retrieval sources. To solve the data heterogeneity caused by multiple data sources, we propose a shared pattern transfer module (SPTM) based on pattern memory and combine the theory of generative adversarial to achieve the semantic representation unbound from modality. Simultaneously, to cope with the lack of annotation data in the RS scenario, multiple unimodal self-supervised frameworks are unified to obtain robust pre-training parameters for the designed MCRN by combining domain alignment and contrastive learning. Finally, we come up with the multi-source triplet loss, the unimodal contrast loss, and the semantic consistency loss, which efficaciously make MCRN achieve competitive results through multitask learning for semantic alignment. We construct multimodal datasets M-RSICD and M-RSITMD, conduct extensive experiments and provide a complete benchmark to facilitate the development of RS multi-source cross-modal retrieval. The code of the MCRN method and the proposed dataset have been open to access at [Link].
Towards Multi-domain Single Image Dehazing via Test-time Training

Recent years have witnessed significant progress in the area of single image dehazing, thanks to the employment of deep neural networks and diverse datasets. Most of the existing methods perform well when the training and testing are conducted on a single dataset. However, they are not able to handle different types of hazy images using a dehazing model trained on a particular dataset. One possible remedy is to perform training on multiple datasets jointly. However, we observe that this training strategy tends to compromise the model performance on individual datasets. Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest. Specifically, during the test time, the helper network evaluates the quality of the dehazing results, then directs the dehazing network to improve the quality by adjusting its parameters via self-supervision. Nevertheless, the inclusion of the helper network does not automatically ensure the desired performance improvement. For this reason, a metalearning approach is employed to make the objectives of the dehazing and helper networks consistent with each other. We demonstrate the effectiveness of the proposed method by providing extensive supporting experiments.
A Control Strategy of Robot Eye-Head Coordinated Gaze Behavior Achieved for Minimized Neural Transmission Noise

Many studies have demonstrated the necessity to drive robot displaying natural and appropriate behaviors in social scenes. In this article, efforts were taken to restore the mechanism of human gaze behavior that can be highly informative for robot-human interaction. In order to determine the rules that biological plants obey in gaze behavior, we modeled the eye-head coordinated gaze behavior as a two degree of freedom synthetic system, and obtained a closed-form equation for determining the movement duration and dynamics of it. By solving the equation of this model numerically under the condition of minimal neural transmission noise effect, it was found that this model can reproduce the gaze shift behavior and predict the coordinated trajectories of eye movement and head torsion. The proposed model and methodology was tested on the Xiaopang robot platform. By directly comparing the experimental result with the practical observation data, it indicates that the proposed model and methodology is robust to represent the pattern of human eye-head coordinated gaze behavior, this concludes that the human gaze sequence has evolved as a strategy to optimize the tradeoff between focal fixation accuracy and gaze shift speed.
Proximity ranking-based multimodal differential evolution

Multimodal optimization aiming at locating multiple global optima at a time has received extensive attention from researchers since it can afford multiple choices for decision-makers. To effectively locate as many optima of multimodal optimization problems (MMOPs) as possible, this paper proposes a novel differential evolution framework, named proximity ranking-based multimodal differential evolution (PRMDE). Firstly, a proximity ranking-based individual selection method is proposed to randomly select parent individuals involved in the mutation operation. Specifically, instead of the classical uniform selection, this paper devises a non-linear weight function to calculate the selection probabilities of individuals according to their proximity rankings and then randomly selects parent individuals based on the roulette wheel selection strategy. In this way, each individual is likely mutated by its Euclidian neighbors. Secondly, an adaptive parameter adjustment strategy is further devised for the selection probability calculation, so that the selection probabilities of closer individuals to each target individual gradually increase as the evolution continues. Thirdly, an adaptive local search strategy is designed to carry out the Gaussian distribution based local search adaptively around individuals. In this way, better individuals have higher chances to conduct local search to subtly improve their quality. By means of the cohesive cooperation among the three main mechanisms, PRMDE is expectedly capable of simultaneously locating multiple optima of MMOPs. Theoretically, any mutation strategy can be embedded into PRMDE to deal with MMOPs. Four classical mutation strategies are adopted in this paper to instantiate PRMDE. Experiments carried out on the publicly acknowledged CEC2013 benchmark MMOP set demonstrate that PRMDE is effective to solve MMOPs and attains considerably competitive or even far better optimization performance than several representative and state-of-the-art multimodal optimization methods.
Barriers to blockchain-based decentralised energy trading: a systematic review

The increasing adoption of clean energy technologies, including solar and wind generation, demand response, energy efficiency, and energy storage (e.g. batteries and electric vehicles) have led to the evolution of the traditional electricity markets from centralised energy trading systems into Distributed Energy Trading (DET) systems. Consequently, savvy business executives are exploring how blockchain might impact their competitive advantage in the emerging DET markets. Due to its salient features of distributed ledger, consensus mechanisms, cryptography, and smart contracts, blockchain technology is being used to provide decentralised trust, immutability, security and privacy, and transparency in DET system. However, integrating blockchain in DET systems is facing technical, administrative, standardisation and economic barriers. Consequently, we seek to conduct a comprehensive market analysis to identify the specific challenges hindering the integration of blockchain in DET systems. Nonetheless, we noticed that there isn't any evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Therefore, in this work we first proposed a conceptual evaluation and review framework for conducting a systematic literature review on blockchain-based DET systems. Then, using the proposed framework, we reviewed the current studies on blockchain-based DET systems to the identify specific challenges hindering the adoption of blockchain and their proposed solutions. Our review found that, although there has been tremendous progress in addressing the technical barriers, the administrative, standardisation and economic barriers have grossly been under reviewed.
Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Abstract Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.
Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems

This research examined the observed datasets and a theoretically derived model for estimating yearly optimum tilt angle (β), maximum incident solar radiation (Hmax), clean gain indicator (CGI), and soiling loss indicator (SLI) at Mumbwa, Zambia, the Mediterranean Region, and low latitude locations across the globe. The cleaned tilted collector emerged as the best performing collector due to Hmax and much higher energy gains compared with the soiled collector. CGI showed an appreciable performance of 0.4737% over -0.4708% on the SLI, indicating that soiling on the surface of photovoltaic (PV) modules significantly depreciates the overall performance of PV modules. Two established empirical models obtained from the literature were compared with the established theoretical model (β=φ). The result revealed that the two models overestimated the observed annual optimum tilt angle in this paper, simply because the models were developed with high latitude location datasets from the Asia continent. However, the newly established monthly and yearly global radiation indicator (GRI) models by the authors in their previous paper performed excellently in the selected representative cities in the Mediterranean region.
Radiology report generation with a learned knowledge base and multi-modal alignment

In clinics, a radiology report is crucial for guiding a patient’s treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.
Cloud computing in supply chain management: Exploring the relationship

This research study addresses the advantages and difficulties of Cloud Computing (CC) in Supply Chain Management (SCM). An overview of the current state of SCM and the difficulties businesses in this sector confront is presented at the beginning of the article. It then explores how cloud-based solutions can address these challenges, such as through the use of real-time data analytics, collaborative platforms, and intelligent automation. Additionally, the paper investigates the potential risks and challenges associated with cloud-based SCM, including data security and privacy concerns, vendor lock-in, and the need for robust disaster recovery plans. To provide a comprehensive understanding of the topic, the paper includes a case study that illustrates how a company successfully implemented cloud-based SCM solutions to improve their operations. The paper concludes by highlighting the key takeaways and insights from the research, and by identifying potential future directions for research in this field. Overall, this study delivers insightful information about the function of CC in SCM and offers useful suggestions for companies looking to use this technology to enhance their supply chain operations.
CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games

We present CLASSQ-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASSQ-L uses a single table for each class of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASSQ-L against a variety of opponents.