dyfan/halluhard-seed-questions huggingface dataset:
The removal of organic micropollutants (OMPs) has been investigated in constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The operation of CWs as BES (CW-BES), either in the form of microbial fuel cells (MFC) or microbial electrolysis cells (MEC), has only been investigated in recent years. The presented experiment used CW meso-scale systems applying a realistic horizontal flow regime and continuous feeding of real urban wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ), diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the removal efficiency of conventional CW systems (CW-control) as well as CW systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although a few positive trends were identified for the CW-BES compared to the CW-control (higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC), these proved to be not statistically significantly different. Mesoscale experiments with real wastewater could thus not confirm earlier positive effects of CW-BES found under strictly controlled laboratory conditions with synthetic wastewaters.

Research question: What factors typically cause discrepancies in micropollutant removal performance between lab-scale experiments with synthetic wastewater and larger-scale or real-wastewater field systems when using bioelectrochemical constructed wetlands?
Inflammatory bowel diseases (IBD) are complex diseases in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when they are exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In the project, the metagenomic data of the gut microbiota of control, Crohn's disease, and ulcerative colitis subjects were divided in three ranges (prevalent, common, uncommon). Then, correlation networks and co-expression networks were used to represent this data. The former networks involved the calculation of the Pearson's correlation and the use of the percolation threshold to binarize the adjacency matrix, whereas the latter involved the construction of the bipartite networks and the monopartite projection after binarization of the biadjacency matrix. Then, centrality measures and community detection were used on the so-built networks. The main results obtained were about the modules of "Bacteroides", which were connected in control subjects' correlation network, "Faecalibacterium prausnitzii", where co-enzyme A became central in IBD correlation networks and "Escherichia coli", which module has different position in the different diagnoses networks.

Research question: How can network-based analyses of gut microbiome data help identify microbial interactions or biomarkers that are useful for diagnosing or treating inflammatory bowel diseases?
Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics.
  This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.

Research question: What practical steps can statistical agencies take to detect, monitor, and mitigate the effects of changing data sources (such as concept drift, bias, or reduced availability) when using machine learning for official statistics?
We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -- visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -- by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models -- achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability.

Research question: How does pretraining on large image–caption datasets improve performance on downstream vision-and-language tasks, and what are the main challenges when transferring such pretrained multimodal models?
On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of $\sim$1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg$^2$ at a luminosity distance of $40^{+8}_{-8}$ Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Msun. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at $\sim$40 Mpc) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over $\sim$10 days. Following early non-detections, X-ray and radio emission were discovered at the transient's position $\sim$9 and $\sim$16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. (Abridged)

Research question: What physical mechanisms can produce a time delay between the gravitational-wave signal from a neutron-star merger and the associated short gamma-ray burst?
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance of person instances. In this paper, we find that context information plays an important role in addressing these issues, and propose a novel method named progressive context refinement (PCR) for human keypoint detection. First, we devise a simple but effective context-aware module (CAM) that can efficiently integrate spatial and channel context information to aid feature learning for locating hard keypoints. Then, we construct the PCR model by stacking several CAMs sequentially with shortcuts and employ multi-task learning to progressively refine the context information and predictions. Besides, to maximize PCR's potential for the aforementioned hard case inference, we propose a hard-negative person detection mining strategy together with a joint-training strategy by exploiting the unlabeled coco dataset and external dataset. Extensive experiments on the COCO keypoint detection benchmark demonstrate the superiority of PCR over representative state-of-the-art (SOTA) methods. Our single model achieves comparable performance with the winner of the 2018 COCO Keypoint Detection Challenge. The final ensemble model sets a new SOTA on this benchmark.

Research question: How can incorporating spatial and channel-wise contextual information improve the accuracy of human keypoint detection in cases of occlusion, blur, and scale variation?
Demand for health care is constantly increasing due to the ongoing demographic change, while at the same time health service providers face difficulties in finding skilled personnel. This creates pressure on health care systems around the world, such that the efficient, nationwide provision of primary health care has become one of society's greatest challenges. Due to the complexity of health care systems, unforeseen future events, and a frequent lack of data, analyzing and optimizing the performance of health care systems means tackling a wicked problem. To support this task for primary care, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models the interactions of patients and primary care physicians on an individual level. By tracking agent interactions, it enables modelers to assess multiple key indicators such as patient waiting times and physician utilization. Based on these indicators, primary care systems can be assessed and compared. Moreover, changes in the infrastructure, patient behavior, and service design can be directly evaluated. To showcase the opportunities offered by SiM-Care and aid model validation, we present a case study for a primary care system in Germany. Specifically, we investigate the effects of an aging population, a decrease in the number of physicians, as well as the combined effects.

Research question: What types of data and validation steps are typically required to ensure an agent-based simulation of primary health care produces reliable, actionable results for policymakers?
In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

Research question: What practical considerations and challenges should be addressed when developing a software platform to implement and evaluate artificial immune systems inspired by innate immunity?
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 B meson decays to muon pairs constrain or rule out models of new physics beyond the Standard Model?
When manufacturing parts using material extrusion additive manufacturing and anisotropic polymers, the mechanical properties of a manufactured component are strongly dependent on the print trajectory orientation. We conduct non-planar slicing and optimize the print trajectories to maximize the alignment between the material deposition direction and the stress flow induced by a predefined load case. The trajectory optimization framework considers manufacturability constraints in the form of uniform layer height and line spacing. We demonstrate the method by manufacturing a load bearing mechanical bracket using a 5-axis 3D printer and a liquid crystal polymer material. The failure strength and stiffness of the optimized bracket are improved by a factor of 44 and 6 respectively when compared with conventional printing.

Research question: How does aligning the extrusion/deposition direction with the principal stress directions of a loaded part improve strength and stiffness in anisotropic polymer prints, and what practical challenges and constraints (e.g., printer kinematics, layer height, line spacing) typically arise when implementing such optimized non-planar printing trajectories?
A lot of business and research effort currently deals with the so called decentralised ledger technology blockchain. Putting it to use carries the tempting promise to make the intermediaries of social interactions superfluous and furthermore keep secure track of all interactions. Currently intermediaries such as banks and notaries are necessary and must be trusted, which creates great dependencies, as the financial crisis of 2008 painfully demonstrated. Especially banks and notaries are said to become dispensable as a result of using the blockchain. But in real-world applications of the blockchain, the power of central actors does not dissolve, it only shifts to new, democratically illegitimate, uncontrolled or even uncontrollable power centers. As interesting as the blockchain technically is, it doesn't efficiently solve any real-world problem and is no substitute for traditional political processes or democratic regulation of power. Research efforts investigating the blockchain should be halted.

Research question: How can democratic accountability and regulatory oversight be effectively maintained or introduced in decentralized ledger systems to prevent concentration of unregulated power?
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI-based image classification solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.

Research question: What are the main barriers to safely and effectively integrating AI-based skin cancer diagnostic tools into routine clinical practice, and how can they be addressed?
An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we focus in particular on global scale migration networks. We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration.

Research question: What are the main ethical and privacy challenges when using geolocated digital data to study large-scale human migration, and what best practices can researchers follow to mitigate those risks?
This paper presents a novel approach to representing task assignments for partitioned agents (respectively, tasks) in distributed systems. A partition of agents (respectively, tasks) is represented by a Young tableau, which is one of the main tools in studying symmetric groups and combinatorics. In this paper we propose a task, agent, and assignment tableau in order to represent a task assignment for partitioned agents (respectively, tasks) in a distributed system. This paper is concerned with representations of task assignments rather than finding approximate or near optimal solutions for task assignments. A Young tableau approach allows us to raise the expressiveness of partitioned agents (respectively, tasks) and their task assignments.

Research question: How can combinatorial structures such as Young tableaux be used to represent and analyze task assignments and partitions of agents in distributed systems, and what practical advantages do they offer compared with more traditional representations?
This work presents a simple and realistic approach to handle the available data of COVID-19 patients in India and to forecast the scenario. The model proposed is based on the available facts like the onset of lockdown (as announced by the Government on 25th day, τ0 and the recovery pattern dictated by a mean life recovery time of τ1 ( normally said to be around 14 days). The data of infected COVID-19 patients from March 2, to April 16, 2020 has been used to fit the evolution of infected, recovery and death counts. A slow rising exponential growth, with R0 close to 1/6, is found to represent the infected counts indicating almost a linear rise. The rest of growth, saturation and decay of data is comprehensibly modelled by incorporating lockdown time controlled R0, having a normal error function like behaviour decaying to zero in some time frame of τ2 . The recovery mean life time τ1 dictates the peak and decay. The results predicted for coming days are interesting and optimistic. The introduced time constants based on experimental data for both the recovery rate as well as for determining the time span of activity of R0 after the lockdown are subject of debate and provide possibility to introduce trigger factors to alter these to be more suited to the model. The model can be extended to other communities with their own R0 and recovery time parameters.

Research question: How does a time-varying reproduction number (R0) that decreases after interventions like lockdowns affect the timing and height of the epidemic peak and the total number of cases?
Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice. New image analysis approaches are needed because tissue section stained with a mixture of protein, DNA and RNA biomarkers are introducing various complexities, including spurious edges due to fluorescent staining artifacts between touching or overlapping cells. We have developed the RRScell method harnessing the stochastic random-reaction-seed (RRS) algorithm and deep neural learning U-net to extract single-cell resolution profiling-map of gene expression over a million cells tissue section accurately and automatically. Furthermore, with the use of manifold learning technique UMAP for cell phenotype cluster analysis, the AI-driven RRScell has equipped with a marker-based image cytometry analysis tool (markerUMAP) in quantifying spatial distribution of cell phenotypes from tissue images with a mixture of biomarkers. The results achieved in this study suggest that RRScell provides a robust enough way for extracting cytometric single cell morphology as well as biomarker content in various tissue types, while the build-in markerUMAP tool secures the efficiency of dimension reduction, making it viable as a general tool in the spatial analysis of high dimensional tissue image.

Research question: What are common strategies and best practices for handling staining artifacts and spurious edges in multiplexed immunofluorescence images to improve accurate single-cell segmentation and downstream phenotype analysis?
Optimal characterization of the mechanical properties of both cells and their surrounding is an issue of major interest. Indeed, cell function and development are strongly influenced by external stimuli. Furthermore, a change in cell mechanics might, in some cases, associate with diseases or malfunctioning. In this work, atomic force microscopy (AFM) was applied to examine the mechanical properties of the silicone elastomer polydimethylsiloxane (PDMS) a common substrate in cell culture. Force spectroscopy analysis was done over different specimens of this elastomeric material containing varying ratios of resin to cross-linker in its structure (5:1, 10:1, 20:1, 30:1 and 50:1), which impacts the final material properties (e.g., stiffness, elasticity). To quantify the mechanical properties of the PDMS, factors as the modulus of Young, the maximum adhesive forces as well as both relaxation amplitudes and times upon constant height contact of the tip (dwell time different of zero) were calculated from the different segments forming the force curves. It is demonstrated that the material stiffness is increased by prior oxygen plasma treatment of the sample, required for hydrophilic switching, contrarily to what observed for its adhesiveness. Subsequent incubation of endothelial HUVEC cells on top of these plasma treated PDMS systems yields minor variation in cell mechanics in comparison to those obtained on a glass reference, on which cells show much higher spreading tendency and, by extension, a remarkable membrane hardening. Thus, surface wettability turns a factor of higher relevance than substrate stiffness inducing variations in the cell mechanics.

Research question: How does substrate surface wettability influence cell mechanical properties (e.g., membrane stiffness and spreading) compared with the effects of substrate stiffness, and what cellular mechanisms mediate these differences?