The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2--10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21--22, 2019 in Alexandria, VA.
Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data is challenging to obtain. However, existing methods face two key limitations: data consistency preservation remains challenging for out-of-domain data, and degradation process alignment is difficult when the degradation mechanism is not mathematically predetermined. To address these issues, this paper presents a novel zero-shot image restoration method (FIR-SDE). Traditional generation-oriented diffusion models (designed for image creation) are replaced with restoration-oriented models (specialized for degradation repair), expanding the range of effectively restorable images. To mitigate the noise offset (discrepancies between real and model-simulated degradation) and to enhance the alignment, a multi-step optimization strategy is employed, which evaluates the distance between real and simulated degraded images via frequency domain distribution. Experiments were conducted on two image restoration tasks (image deraining and inpainting) using three public datasets (AFHQ-dog, CelebA, and FFHQ), with Gaussian blur and motion blur superimposed as noise offsets. Results demonstrate that FIR-SDE method outperforms competitive methods in restoration quality and noise resistance. By eliminating data space constraints and exhibiting robustness against noise offsets, FIR-SDE offers a more flexible and efficient solution to broaden the practical applicability of zero-shot image restoration.
Electronic computers. Computer science, Information technology
Underwater vision is fundamental to ocean exploration, yet it is frequently impaired by underwater degradation including low contrast, color distortion and blur, thereby presenting significant challenges for underwater object detection (UOD). Most existing methods employ underwater image enhancement as a preprocessing step to improve visual quality prior to detection. However, image enhancement and object detection are optimized for fundamentally different objectives, and directly cascading them leads to feature distribution mismatch. Moreover, prevailing dual-branch architectures process enhancement and detection independently, overlooking multi-scale interactions across domains and thus constraining the learning of cross-domain feature representation. To overcome these limitations, We propose an underwater cross-domain fusion Transformer detector (UCF-DETR). UCF-DETR jointly leverages image enhancement and object detection by exploiting the complementary information from the enhanced and original image domains. Specifically, an underwater image enhancement module is employed to improve visibility. We then design a cross-domain feature pyramid to integrate fine-grained structural details from the enhanced domain with semantic representations from the original domain. Cross-domain query interaction mechanism is introduced to model inter-domain query relationships, leading to accurate object localization and boundary delineation. Extensive experiments on the challenging DUO and UDD benchmarks demonstrate that UCF-DETR consistently outperforms state-of-the-art methods for UOD.
Gender imbalance persists across science, technology, engineering, and mathematics (STEM) fields, including computer science, where it appears in researcher demographics, productivity, recognition, hiring, and career progression. Given computer science's rapid expansion and global influence, addressing this imbalance is essential for broadening participation and fueling innovation. Although journal-oriented disciplines exhibit consistent gender imbalances in citation practices, it remains unclear whether similar patterns arise in the conference-centric culture of computer science. Here, we systematically investigate gender imbalance in citations of conference and journal papers in computer science. We find that papers for which a woman is listed as either first or last author receive fewer citations than expected, partly because of homophilic citation tendencies (i.e., authors tend to cite papers that share specific attributes). This imbalance is especially pronounced for conference papers--particularly those published at top-tier venues--relative to journals. Moreover, we find that the prominence of the first or last author and the structure of their local co-authorship networks are potential drivers of these imbalances. By exploring how conference-centric publishing practices can amplify systemic imbalances in computer science, our study offers insights that may inform efforts to foster more equitable representation in academia.
I describe my experience writing the first original, modern Computer Science research paper expressed entirely in an Indian language. The paper is in Telugu, a language with approximately 100 million speakers. The paper is in the field of distributed computing and it introduces a technique for proving epistemic logic based lower bounds for multiprocessor algorithms. A key hurdle to writing the paper was developing technical terminology for advanced computer science concepts, including those in algorithms, distributed computing, and discrete mathematics. I overcame this challenge by deriving and coining native language scientific terminology through the powerful, productive, Pāninian grammar of Samskrtam. The typesetting of the paper was an additional challenge, since mathematical typesetting in Telugu is underdeveloped. I overcame this problem by developing a Telugu XeLaTeX template, which I call TeluguTeX. Leveraging this experience of writing an original computer science research paper in an Indian language, I lay out a vision for how to ameliorate the state of scientific writing at all levels in Indic languages -- languages whose native speakers exceed one billion people -- through the further development of the Sanskrit technical lexicon and through technological internationalization.
Jonnel Alejandrino, Elmer Dadios, Ryan Rhay Vicerra
et al.
Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (<i>Fragaria × ananassa</i> ‘<i>Sweet Charlie</i>’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture.
George Tsamis, Georgios Evangelos, Aris Papakostas
et al.
One significant digital initiative that is changing Greece’s tax environment is the myDATA platform. The platform, which is a component of the wider digital governance agenda, provides significant added value to enterprises and the tax administration, despite the challenges of adaption. Despite the positive response, we find that the development of the platform could have been carried out quickly and at a significantly lower cost and could have been able to cope much faster with the rapid and necessary changes that the platform will have to comply with. For these reasons, development in WordPress would be considered essential as this CMS platform guarantees a fast and developer-friendly environment. In this publication, as a contribution, we provide all the necessary information to develop a myDATA-like platform in a fast, economical and functional way using the WordPress CMS. Our contribution also contains the analysis of the minimum necessary amount of services of the myDATA platform in order to perform its basic functionalities, the description of the according database relational model, which must be implemented in order to provide the same functionality with the myDATA platform, and the analysis of available methods to quickly create the necessary forms and services. In addition, we study how to develop Artificial Intelligence mechanisms with a success rate reaching up to 90% for automatic tax violation detection algorithms.
Vogelsang Tobias, Fahlbusch Fabian B., Behr Anna-Lena
et al.
Early diagnosis of neurodevelopmental disorders in infants relies on accurate analysis of spontaneous movements. Achieving this requires fast and precise pose estimation methods tailored to infant-specific anatomy and motion. This study evaluates several pretrained YOLOv11-pose models for pose estimation in depth video recordings of preterm neonates and infants using the open source babyPose data set database. The fastest model (YOLOv11n-pose) has a inference time of 0.007 seconds. Considering a previously proposed data split without subject-wise separation between training and testing data, the most accurate model (YOLOv11m-pose) has a median root mean squared distance (RMSD) of 2.15. The median Dice Similarity Coefficient (DSC) and Recall (R) of the joints are 0.85 and 0.86, while the median DSC and R of the joint connections are 0.90 and 0.91. Considering a subject-wise separation of training and testing data, the results noticeably degrade, e.g. to a median DSC and R of the joints of 0.79 and 0.81, while the median DSC and R of the joint connections are 0.75 and 0.79. The present work demonstrates a fast and, copared to the literature, accurate approach to depth-based pose estimation in preterm neonates and infants paving the way for automated movement analysis as a clinical tool for early detection of developmental impairments. Particularly in semiautomated settings where subject-specific annotations can be provided, the results are convining. Regarding the abilities to generalize, more work is required to improve the results.
Portfolio optimization is a process that involves distribution of capital with the purpose of maximizing returns and at the same time minimizing risks. The current paper discusses the use of Transformer networks in supervised learning for portfolio optimization which can set new standards for machine learning-based investment strategies. The experiments show that the portfolio management method that utilizes attention mechanisms goes beyond traditional optimization methods with a substantial difference. The performance of the recommended model in terms of average annualized return and Sharpe ratio was 24.8% and 1.69 respectively over the 14 test cases. These are considerable improvements over the benchmark strategies like equal-weighted portfolios (Sharpe ratio: 0.54), market capitalization-weighted portfolios (Sharpe ratio: 0.43), and traditional index portfolios (Sharpe ratio: 0.37). The attention mechanism is what makes the model able to dynamically adjust the portfolio weights according to the changing market forces, thus, it can blend active and passive investments efficiently. Moreover, it managed to maintain a very good risk control capacity with a Sortino ratio of 2.45 while its performance during market volatility was still quite good. So, this research serves to provide both quantitative finance and machine learning with a proof that the novel deep learning architectures can easily beat the conventional portfolio optimization methods, even in the case of small asset pools.
Abstract Unlike single-image steganography, the scheme of payload distribution on different images plays a pivotal role in the security performance of multi-image steganography. In this paper, a novel multi-image steganography scheme: image stitching sender (ISS) is proposed, which achieves optimal payload distribution by optimizing the stitching scheme of multi-cover-images. In the ISS scheme, we employ peak signal-to-noise ratio as the similarity evaluation metric for the stitched cover image and stego image. Besides, genetic algorithm is used to find the local optimal solution for the similarity, corresponding to a locally optimal multi-image steganographic stitching scheme. The experiment demonstrates that ISS exhibits enhanced anti-detection capabilities in comparison to other multi-image steganography schemes. Furthermore, when combined with non-additive embedding methods, the ISS can achieve a more substantial improvement in security compared to additive embedding methods.
The increasing complexity of computer science research projects demands more effective tools for deploying code repositories. Large Language Models (LLMs), such as Anthropic Claude and Meta Llama, have demonstrated significant advancements across various fields of computer science research, including the automation of diverse software engineering tasks. To evaluate the effectiveness of LLMs in handling complex code development tasks of research projects, particularly for NLP/CV/AI/ML/DM topics, we introduce CSR-Bench, a benchmark for Computer Science Research projects. This benchmark assesses LLMs from various aspects including accuracy, efficiency, and deployment script quality, aiming to explore their potential in conducting computer science research autonomously. We also introduce a novel framework, CSR-Agents, that utilizes multiple LLM agents to automate the deployment of GitHub code repositories of computer science research projects. Specifically, by checking instructions from markdown files and interpreting repository structures, the model generates and iteratively improves bash commands that set up the experimental environments and deploy the code to conduct research tasks. Preliminary results from CSR-Bench indicate that LLM agents can significantly enhance the workflow of repository deployment, thereby boosting developer productivity and improving the management of developmental workflows.
The study of boundary layer flow (BLF) with heat-mass transfer of binary chemical processes and nanofluids (NF) over a wedge is essential for improving heat transfer and reaction kinetics in applications including processing of material technologies, chemical reactors, and energy-efficient cooling mechanisms. This paper examines the entropy optimized BLF of silver Ag− water based nanofluid with binary chemical species over a wedge surface. The Tiwari-Das model is executed in this model which account the load of Ag− nanomaterials. The flow of NF over a moving wedge subject to favorable and adverse pressure differential is addressed by Naiver-Stokes equation. This model accounts the homogeneous heat reaction, viscous dissipation, joule heating and thermal radiations. The dimensionless equations for flow, for heat, and concentration are formulated and solved numerically using the fourth ordered Rung-Kutta approach. The findings suggest that fluid concentration is lowered with a rise in Schmidt number and homogenous chemical reaction. Thermal distribution improve with heterogonous reaction, magnetic parameter and deteriorate with wedge parameter. The skin friction rises from 25.277 % to 26.455 % with a material load of 3 % and magnetic parameter. The Nusselt decline with a radiative parameter from 10.984 % to 2.9748 % when particle load of 3 % is accounted.
Koneru Suvarna Vani, Puppala Praneeth, Vivek Kommareddy
et al.
Human vision depends heavily on retinal tissue. The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all. Additionally, the diagnosis is susceptible to inaccuracies when a large dataset is involved. Therefore, a fully automated transfer learning approach for diagnosing diabetic retinopathy (DR) is suggested to minimize human intervention while maintaining high classification accuracy. To address this issue, we proposed a transfer learning-based trilateral attention network (TaNet) for the classification. To boost the visual quality of the DR pictures, a contrast constrained adaptive histogram equalization approach is applied. The pre-processed pictures are then segmented using a bilateral segmentation network (BiSeNet). The BiSeNet segmented the optic disc and blood vessels individually. After the completion of segmentation, the features are extracted. Feature extraction is based on the wavelet scattering transformation approach. The results of many trials were evaluated against the Messidor-2, EYEPACS, and APTOS 2019 datasets. The proposed model was created using a refined pre-trained technique and transfer learning methodology. Finally, the suggested framework was tested using efficiency assessment methods, and the classification rate was recorded as having above 98% sensitivity, specificity, precision, and accuracy. The proposed approach yields greater performance and shows enhancement towards the existing approach.
This research presents an improvement to the traditional solar updraft tower, which relies solely on solar energy and cannot operate continuously throughout the day. The enhancement involves a hybrid energy approach by installing a vortex generator at the top of the tower to convert crosswinds into a vortex flow at the chimney’s top. This modification induces an updraft within the tower, enabling it to generate electricity continuously, even at night when there is no sunlight. The aim is to enable the solar updraft tower to harness crosswind energy without altering the tower’s main structure. This involves developing a vortex generator from a unidirectional wind intake design to a three-directional intake, enhancing the feasibility of commercial installation. Additionally, various designs and heights of vortex generators were developed, considering different crosswind speeds (2, 4, 6, and 8 m/s). The research utilizes the finite element method, along with real model construction, to validate the reliability of the study’s findings. The results indicate that the updraft speed is directly proportional to the crosswind speed. From a physical standpoint, the vortex generator with a height equal to D produced the best results in all experiments. The square, cylindrical, and diffuser shapes increased the wind speed inside the chimney by 60%, 41%, and 48%, respectively. These results from various shapes provide effective design and development guidelines for the future commercial use of vortex generators.
As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.