dyfan/halluhard-seed-questions huggingface dataset:
Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.

Research question: What are the practical challenges and limitations of deploying programmable metasurfaces in real-world wireless environments, including power, control, scalability, and maintenance?
We present work on sentiment analysis in Twitter for Macedonian. As this is pioneering work for this combination of language and genre, we created suitable resources for training and evaluating a system for sentiment analysis of Macedonian tweets. In particular, we developed a corpus of tweets annotated with tweet-level sentiment polarity (positive, negative, and neutral), as well as with phrase-level sentiment, which we made freely available for research purposes. We further bootstrapped several large-scale sentiment lexicons for Macedonian, motivated by previous work for English. The impact of several different pre-processing steps as well as of various features is shown in experiments that represent the first attempt to build a system for sentiment analysis in Twitter for the morphologically rich Macedonian language. Overall, our experimental results show an F1-score of 92.16, which is very strong and is on par with the best results for English, which were achieved in recent SemEval competitions.

Research question: What are the main challenges in performing sentiment analysis on morphologically rich languages in social media texts, and what general strategies can be used to address them?
Two-dimensional (2D) materials have attracted increasing research interest because of the abundant choice of materials with diverse and tunable electronic, optical, and chemical properties. Moreover, 2D material based heterostructures combining several individual 2D materials provide unique platforms to create an almost infinite number of materials and show exotic physical phenomena as well as new properties and applications. To achieve these high expectations, methods for the scalable preparation of 2D materials and 2D heterostructures of high quality and low cost must be developed. Chemical vapor deposition (CVD) is a powerful method which may meet the above requirements, and has been extensively used to grow 2D materials and their heterostructures in recent years, despite several challenges remaining. In this review of the challenges in the CVD growth of 2D materials, we highlight recent advances in the controlled growth of single crystal 2D materials, with an emphasis on semiconducting transition metal dichalcogenides. We provide insight into the growth mechanisms of single crystal 2D domains and the key technologies used to realize wafer-scale growth of continuous and homogeneous 2D films which are important for practical applications. Meanwhile, strategies to design and grow various kinds of 2D material based heterostructures are thoroughly discussed. The applications of CVD-grown 2D materials and their heterostructures in electronics, optoelectronics, sensors, flexible devices, and electrocatalysis are also discussed. Finally, we suggest solutions to these challenges and ideas concerning future developments in this emerging field.

Research question: What are the main technical challenges and limitations that must be overcome to reliably produce wafer-scale, high-quality 2D materials and heterostructures using chemical vapor deposition?
Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29\% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. In this paper, we explore the potential of enhancing PCa gland segmentation using a novel MRI modality called synthetic correlated diffusion imaging (CDI$^s$). We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment prostate glands from a 200 CDI$^s$ patient cohort. We find that SegResNet achieved superior segmentation performance with a Dice-Sorensen coefficient (DSC) of $76.68 \pm 0.8$. Notably, the Attention U-Net, while slightly less accurate (DSC $74.82 \pm 2.0$), offered a favorable balance between accuracy and computational efficiency. Our findings demonstrate the potential of deep learning models in improving prostate gland segmentation using CDI$^s$ to enhance PCa management and clinical support.

Research question: How could more accurate automatic prostate gland segmentation from MRI change clinical workflow, diagnosis, and treatment planning for prostate cancer?
In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax and Angular Softmax have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW BLUFR and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available at https://github.com/happynear/AMSoftmax

Research question: How does adding an angular margin to a softmax-based loss function influence intra-class compactness and inter-class separation in learned face embeddings, and why is feature normalization important when using such margin-based losses?
Cloning spare parts and entities of mass products is an old and serious unsolved problem for the automotive industry. The economic losses in addition to a loss of know-how and IP theft as well as security and safety threats are huge in all dimensions. This presentation gives an overview of the traditional state of the art on producing clone resistant electronic units in the last two decades. A survey is attempting to demonstrate the techniques so far known as Physically Unclonable Functions PUFs showing their advantages and drawbacks. The necessity for fabricating mechatronic-security in the vehicular environment is emerging to become a vital requirement for new automotive security regulations (legal regulations) in the near future. The automotive industry is facing a challenge to produce low-cost and highly safe and secure networked automotive systems. The emerging networked smart traffic environment is offering new safety services and creating at the same time new needs and threats in a highly networked world. There is a crying need for automotive security that approaches the level of the robust biological security for cars as dominating mobility actors in the modern smart life environment. Possible emerging technologies allowing embedding practical mechatronic-security modules as a low-cost digital alternative are presented. Such digital clone-resistant mechatronic-units (as Electronic Control Units ECUs) may serve as smart security anchors for the automotive environment in the near future. First promising initial results are also presented.

Research question: What are the main technical and practical challenges in designing low-cost, clone-resistant hardware modules for vehicles, and what strategies can be used to address them?
In materials science, "green" synthesis has gained extensive attention as a reliable, sustainable, and eco-friendly protocol for synthesizing a wide range of materials/nanomaterials including metal/metal oxides nanomaterials, hybrid materials, and bioinspired materials. As such, green synthesis is regarded as an important tool to reduce the destructive effects associated with the traditional methods of synthesis for nanoparticles commonly utilized in laboratory and industry. In this review, we summarized the fundamental processes and mechanisms of "green" synthesis approaches, especially for metal and metal oxide [e.g., gold (Au), silver (Ag), copper oxide (CuO), and zinc oxide (ZnO)] nanoparticles using natural extracts. Importantly, we explored the role of biological components, essential phytochemicals (e.g., flavonoids, alkaloids, terpenoids, amides, and aldehydes) as reducing agents and solvent systems. The stability/toxicity of nanoparticles and the associated surface engineering techniques for achieving biocompatibility are also discussed. Finally, we covered applications of such synthesized products to environmental remediation in terms of antimicrobial activity, catalytic activity, removal of pollutants dyes, and heavy metal ion sensing.

Research question: What are the main challenges and limitations in scaling up green synthesis of metal and metal-oxide nanoparticles using natural extracts for industrial applications?
Integrating deep learning with clinical expertise holds great potential for addressing healthcare challenges and empowering medical professionals with improved diagnostic tools. However, the need for annotated medical images is often an obstacle to leveraging the full power of machine learning models. Our research demonstrates that by combining synthetic images, generated using diffusion models, with real images, we can enhance nonalcoholic fatty liver disease (NAFLD) classification performance even in low-data regime settings. We evaluate the quality of the synthetic images by comparing two metrics: Inception Score (IS) and Fréchet Inception Distance (FID), computed on diffusion- and generative adversarial network (GAN)-generated images. Our results show superior performance for the diffusion-generated images, with a maximum IS score of $1.90$ compared to $1.67$ for GANs, and a minimum FID score of $69.45$ compared to $100.05$ for GANs. Utilizing a partially frozen CNN backbone (EfficientNet v1), our synthetic augmentation method achieves a maximum image-level ROC AUC of $0.904$ on a NAFLD prediction task.

Research question: How can synthetic medical images generated by deep generative models be validated and regulated to ensure they are safe, unbiased, and clinically useful when used to augment training data for diagnostic AI systems?
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Research question: How can computational methods and machine learning be used to identify, prioritize, and optimize bioactive compounds from complex plant extracts for drug development?
In this paper, a quasi-asymmetric Doherty power amplifier (PA) is designed without load modulation using the GaAs 0.25μm pHEMT technology to reach an enlarged output power back-off (OPBO) with circuitry solutions in order to overcome technology restrictions. To prevent power leakage in auxiliary PA (PAaux) due to its extremely large off-state impedance, a Wilkinson power combiner is added to the output. Moreover, an input asymmetric power divider is designed to guarantee that no considerable power is delivered to main PA (PAmain) in the high-power region to make it saturated. A two-section matching network is proposed for PAmain, which simultaneously compensates for phase differences of the main and auxiliary amplification paths. To control the significant impedance variation of PAaux versus sweeping power and the different impedance trajectories of the main and auxiliary amplification paths, the bias and dimension selection of PAaux are analyzed to reach the desired output power profile versus input power. These methods overcome impedance variations and linearity degradation. To achieve the aimed 10% fractional bandwidth, appropriate low-quality LC-networks are selected as matching networks. The simulation results indicate the utility of the proposed structure for microwave link applications. Continuous-wave simulations imply that the Doherty PA has a 33dBm maximum output power and a 13.5dB power gain with less than 1dB power gain compression in the desired frequency range (7.6-8.4GHz). The drain efficiency of 30% at the highest input power, minimum of second and third harmonic powers of -140dBm and -130dBm, respectively, and OPBO of 7.5dB are also obtained.

Research question: What circuit-level techniques are commonly used to extend the output power back-off of Doherty power amplifiers while preserving efficiency and linearity?
The electronic excited states of molecular aggregates and their photophysical signatures have long fascinated spectroscopists and theoreticians alike since the advent of Frenkel exciton theory almost 90 years ago. The influence of molecular packing on basic optical probes like absorption and photoluminescence was originally worked out by Kasha for aggregates dominated by Coulombic intermolecular interactions, eventually leading to the classification of J- and H-aggregates. This review outlines advances made in understanding the relationship between aggregate structure and photophysics when vibronic coupling and intermolecular charge transfer are incorporated. An assortment of packing geometries is considered from the humble molecular dimer to more exotic structures including linear and bent aggregates, two-dimensional herringbone and "HJ" aggregates, and chiral aggregates. The interplay between long-range Coulomb coupling and short-range charge-transfer-mediated coupling strongly depends on the aggregate architecture leading to a wide array of photophysical behaviors.

Research question: How can experimental spectroscopic techniques distinguish the effects of long-range Coulomb (Frenkel) coupling from short-range charge-transfer interactions in molecular aggregates?
Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. Furthermore, Tuberculosis (TB) remains a major health problem in several low- and middle-income countries and its common symptoms include fever, cough and tiredness, similarly to COVID-19. In order to help in the detection of COVID-19, we propose the extraction of deep features (DF) from chest X-ray images, a technology available in most hospitals, and their subsequent classification using machine learning methods that do not require large computational resources. We compiled a five-class dataset of X-ray chest images including a balanced number of COVID-19, viral pneumonia, bacterial pneumonia, TB, and healthy cases. We compared the performance of pipelines combining 14 individual state-of-the-art pre-trained deep networks for DF extraction with traditional machine learning classifiers. A pipeline consisting of ResNet-50 for DF computation and ensemble of subspace discriminant classifier was the best performer in the classification of the five classes, achieving a detection accuracy of 91.6+ 2.6% (accuracy + 95% Confidence Interval). Furthermore, the same pipeline achieved accuracies of 98.6+1.4% and 99.9+0.5% in simpler three-class and two-class classification problems focused on distinguishing COVID-19, TB and healthy cases; and COVID-19 and healthy images, respectively. The pipeline was computationally efficient requiring just 0.19 second to extract DF per X-ray image and 2 minutes for training a traditional classifier with more than 2000 images on a CPU machine. The results suggest the potential benefits of using our pipeline in the detection of COVID-19, particularly in resource-limited settings and it can run with limited computational resources.

Research question: How reliable are chest X-ray–based machine learning systems at distinguishing COVID-19 from other respiratory diseases (such as tuberculosis and bacterial/viral pneumonia) in low-resource clinical settings, and what are the common sources of diagnostic errors and limitations?
A preference for ethane Industrial production of ethylene requires its separation from ethane in a cryogenic process that consumes large amounts of energy. An alternative would be differential sorption in microporous materials. Most of these materials bind ethylene more strongly that ethane, but adsorption of ethane would be more efficient. Li et al. found that a metal-organic framework containing iron-peroxo sites bound ethane more strongly than ethylene and could be used to separate the gases at ambient conditions. Science , this issue p. 443

Research question: What material properties and binding sites determine whether a microporous adsorbent will preferentially capture ethane rather than ethylene, and how can these be engineered to enable efficient ambient-temperature separation?
The presence of an expanded polyglutamine produces a toxic gain of function in huntingtin. Protein aggregation resulting from this gain of function is likely to be the cause of neuronal death. Two main mechanisms of aggregation have been proposed: hydrogen bonding by polar-zipper formation and covalent bonding by transglutaminase-catalyzed cross-linking. In cell culture models of Huntington's disease, aggregates are mostly stabilized by hydrogen bonds, but covalent bonds are also likely to occur. Nothing is known about the nature of the bonds that stabilize the aggregates in the brain of patients with Huntington's disease. It seems that the nature of the bond stabilizing the aggregates is one of the most important questions, as the answer would condition the therapeutic approach to Huntington's disease.

Research question: What experimental methods can be used to determine whether protein aggregates in human neurodegenerative disease are stabilized mainly by hydrogen bonds (e.g., polar zippers) or by covalent cross-links (e.g., transglutaminase-mediated)?
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIN based recommendation</i> . It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.

Research question: How can embeddings learned from heterogeneous information networks be effectively integrated into recommendation models to improve accuracy and handle cold-start users or items?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare decays like Bs→μ+μ− and B0→μ+μ− constrain extensions of the Standard Model, and which classes of new-physics models are most strongly affected by such measurements?