Sufficient Context Research Articles

Is There a Third Text in Zeina Hashem Beck’s Duets? Translingual Poetics in O

This article traces Zeina Hashem Beck’s development of a new poetic form called the “duet.” Duets include equal parts written in two different languages, English and Arabic for the Lebanese poet whose recent collection, O, includes six such duets. The ideal bilingual reader brings sufficient context in both languages to the understanding of the dialogue between the two duet halves. This study argues, however, that access to the third text hidden in this dialogue is not exclusive to those equally familiar with the different represented languages. Close readings of “daily كل يوم” and “blue أزرق” identify the characteristics and variations of the new poetic form. Analysis of “prophecy نبوة” and “Ode to Babel نشيد الانتظار” centers around translingual poetics. Scrutiny of “Dear white critic, رفيقي في الرحيل,” and “Ode to Leaving غربة” highlights the impact of projected audiences on the duet development. Relying on a comparative analysis of these six examples, the article discusses the benefits of ever improving machine translation, the availability of native speaker consultations, as well as a growing polyglot imaginary as extensions of traditional tools conducive to poetry exegesis.

KnotFold: Improving Peptide Structure Predictions with Simulated Coevolution

We present a novel approach for embedding experimental contact information in the protein structure prediction tool AlphaFold2 that harnesses the principles of protein sequence coevolution to enforce disulfide connectivity patterns in the prediction of disulfide-rich peptide (DRP) structures. While AlphaFold2 generates accurate DRP structure prediction in most cases, it sometimes fails at predicting the specific connectivity pattern of the multiple disulfide bonds. Here, we take advantage of the principles of sequence coevolution to engineer the multiple sequence alignment (MSA) input with specific connectivity patterns by mutating highly conserved cysteines in subsets of the MSA—which we term “simulated coevolution.” Engineering the MSA prompt with simulated coevolution provides sufficient context in most cases for AlphaFold2 to obey the provided connectivity. Our approach—KnotFold—can be used to incorporate experimental disulfide connectivity patterns from mass spectrometry into DRP structure prediction. Lastly, we find that peptide properties and AF2Rank Composite Confidence scores can be used to differentiate native from non-native connectivity patterns in some cases, improving the ability to determine native connectivity of DRPs from sequence alone. Published by the American Physical Society 2024

Supply Skepticism or Supply Realism?

While Been et al. makes a strong case that new market‑rate supply can affect rents and prices, what is missing from the piece is sufficient context on who benefits, to what extent and whether market reforms and high‑end rental housing can adequately move the housing affordability needle for low‑income renters. At best, current research suggests new market‑rate rental supply slightly lowers the rate of rent increases for all, at worst it may also increase rents in the short‑run in lower quality housing occupied by lower‑income renters. While the authors critique “supply skepticism,” I call for a middle path of supply realism. Supply realism acknowledges the racist history of exclusionary housing policies, embraces zoning reforms while also being clear eyed about the limitations of zoning and other market reforms alone to achieve universal housing affordability.

Prediction of tumor board procedural recommendations using large language models.

Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival. METHODS: We evaluated the quality and accuracy of predictions by several large language models for recommending procedures by a Head and Neck Oncology tumor board, which we adapted for the task using parameter-efficient fine-tuning or in-context learning. Records were divided into two sets: n=229 used for training and n=100 records for validation of our approaches. Randomized, blinded, manual human expert classification was used to evaluate the different models. RESULTS: Treatment line congruence varied depending on the model, reaching up to 86%, with medically justifiable recommendations up to 98%. Parameter-efficient fine-tuning yielded better outcomes than in-context learning, and larger/commercial models tend to perform better. Providing precise, medically justifiable procedural recommendations for complex oncology patients is feasible. Extending the data corpus to a larger patient cohort and incorporating the latest guidelines, assuming the model can handle sufficient context length, could result in more factual and guideline-aligned responses and is anticipated to enhance model performance. We, therefore, encourage further research in this direction to improve the efficacy and reliability of large language models as support in medical decision-making processes.

CAFNet: Context aligned fusion for depth completion

Depth completion aims at reconstructing a dense depth from sparse depth input, frequently using color images as guidance. The sparse depth map lacks sufficient contexts for reconstructing focal contexts such as the shape of objects. The RGB images contain redundant contexts including details useless for reconstruction, which reduces the efficiency of focal context extraction. The unaligned contextual information from these two modalities poses a challenge to focal context extraction and further fusion, as well as the accuracy of depth completion. To optimize the utilization of multimodal contextual information, we explore a novel framework: Context Aligned Fusion Network (CAFNet). CAFNet comprises two stages: the context-aligned stage and the full-scale stage. In the context-aligned stage, CAFNet downsamples input RGB-D pairs to the scale, at which multimodal contextual information is adequately aligned for feature extraction in two encoders and fusion in CF modules. In the full-scale stage, feature maps with fused multimodal context from the previous stage are upsampled to the original scale and subsequentially fused with full-scale depth features by the GF module utilizing a dynamic masked fusion strategy. Ultimately, accurate dense depth maps are reconstructed, leveraging the GF module’s resultant features. Experiments conducted on indoor and outdoor benchmark datasets show that the CAFNet produces results comparable to state-of-the-art methods while effectively reducing computational costs.

Timely, Granular, and Actionable: Designing a Social Listening Platform for Public Health 3.0

Every day, patients access and generate online health content through a variety of channels, creating an ever-expanding sea of digital data. At the same time, proponents of public health have recently called for timely, granular, and actionable data to address a range of public health issues, stressing the need for social listening platforms that can identify and compile this valuable data. Yet previous attempts at social listening in healthcare have yielded mixed results, largely because they have failed to incorporate sufficient context to understand the communications they seek to analyze. Guided by activity theory to design HealthSense, we propose a platform for efficiently sensing and gathering data across the web for real-time analysis to support public health outcomes. HealthSense couples theory-guided content analysis and graph propagation with graph neural networks (GNNs) to assess the relevance and credibility of information, as well as intelligently navigate the complex online channel landscape, leading to significant improvements over existing social listening tools. We demonstrate the value of our artifact in gathering information to support two exemplar public health tasks: (1) performing postmarket drug surveillance for adverse reactions and (2) addressing the opioid crisis by monitoring for potent synthetic opioids released into communities. Our results across data, user, and event experiments show that effective design artifacts can enable better outcomes across both automated and human decision-making contexts, making social listening for public health possible, practical, and valuable. Through our design process, we extend activity theory to address the complexities of modern online communication platforms, where information resides not only in the collection of individual communication activities but also in the complex network of interactions among them.

Providing biological context for GWAS results using eQTL regulatory and co-expression networks in Populus.

Our study utilized genome-wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co-expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks.

State-of-the-Art Review: Evaluation and Management of Delusional Infestation.

Delusional infestation is a condition encountered frequently by healthcare professionals across a variety of specialties in which patients have a fixed, false belief that they are infested with living creatures, such as bugs, parasites, worms, or mites, or nonliving objects, such as fibers. Delusional infestation can be debilitating for patients, who not only present with intense psychological distress and physical discomfort but are also at risk of developing numerous dermatological and psychiatric complications. This condition poses unique diagnostic challenges, as these symptoms can occur secondary to many metabolic or infectious causes, as well as unique treatment challenges, with patients frequently refusing psychiatric care and consequently seeking evaluation by other healthcare professionals. In this review, we aim to use existing literature to provide clinicians in infectious diseases or other specialties with sufficient clinical context and treatment guidance for the appropriate management of delusional infestation.

Topic modeling for short texts: comparative analysis of algorithms

The steady increase in the popularity of social media as a means of communication actualizes methodological issues related to processing of short texts with less semantic context than large corpora, which are widely used for training and testing machine learning models for textual data. Topic modeling, an unsupervised machine learning technique aimed at aggregating texts into topic clusters, has many academic and practical applications where information on true groupings of texts is not available. However, the performance of topic modeling algorithms may be limited by requirement of a sufficient semantic context for a high-quality numerical representation of a unit of text, which may not be derived effectively from a short document. This paper is dedicated to discussing 6 different approaches to topic modeling, comparing their performance on a set of Russian-language comments on TikTok and formally evaluating their performance based on speed and coherence of the resulting topics.

Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease data sets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and mechanobiology. Our 3D convolutional neural network recognized TMJ disorder patients through participant-specific morphological features in condylar, ramus, and chin. Driven by deep learning model outputs, biomechanical modeling revealed that small mandibular size and flat condylar shape were associated with increased TMJ disorder risk through increased joint force, decreased tissue nutrient availability and cell ATP production, and increased TMJ disc strain energy density. Combining explainable deep learning and multiscale biomechanical modeling addresses the "mechanism unknown" limitation undermining translational confidence in clinical applications of deep learning and increases methodological accessibility for smaller clinical data sets by providing the crucial biomechanical context.

Real-World Treatment Patterns and Clinical Outcomes Among Patients with Metastatic or Unresectable EGFR-Mutated Non-Small Cell Lung Cancer Previously Treated with Osimertinib and Platinum-Based Chemotherapy.

For patients with epidermal growth factor receptor-mutated (EGFRm) locally advanced/metastatic non-small cell lung cancer (mNSCLC) whose disease has progressed on or after osimertinib and platinum-based chemotherapy (PBC), no uniformly accepted standard of care exists. Moreover, limited efficacy of standard treatments indicates an unmet medical need, which is being addressed by ongoing clinical investigations, including the HERTHENA-Lung01 (NCT04619004) study of patritumab deruxtecan (HER3‑DXd). However, because limited information is available on real-world clinical outcomes in such patients, early-phase trials of investigational therapies lack sufficient context for comparison. This study describes the real-world clinical characteristics, treatments, and outcomes for patients with EGFRm mNSCLC who initiated a new line of therapy following previous osimertinib and PBC, including a subset matched to the HERTHENA-Lung01 population. This retrospective analysis used a US database derived from deidentified electronic health records. The reference cohort included patients with EGFRm mNSCLC who had initiated a new line of therapy between November 13, 2015 and June 30, 2021, following prior osimertinib and PBC. A subset of patients resembling the HERTHENA-Lung01 population was then extracted from the reference cohort; this matched subset was optimized using propensity score (PS) weighting. Endpoints were real-world overall survival (rwOS) and real-world progression-free survival (rwPFS). Confirmed real-world objective response rate (rwORR; partial/complete response confirmed ≥ 28days later) was calculated for the response-evaluable subgroups of patients (with ≥ 2 response assessments spaced ≥ 28days apart). In the reference cohort (N = 273), multiple treatment regimens were used, and none was predominant. Median rwPFS and rwOS were 3.3 and 8.6months, respectively; confirmed rwORR (response evaluable, n = 123) was 13.0%. In the matched subset (n = 126), after PS weighting, median rwPFS and rwOS were 4.2 and 9.1months, respectively; confirmed rwORR (response evaluable, n = 57) was 14.1%. The treatment landscape for this heavily pretreated population of patients with EGFRm mNSCLC is fragmented, with no uniformly accepted standard of care. A high unmet need exists for therapeutic options that provide meaningful improvements in clinical benefit.

FocusDet: an efficient object detector for small object

The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative.

Design and Implementation of Chinese Language Teaching System Based on Virtual Reality Technology

Traditional Chinese language teaching methods have always had problems, such as a lack of practical opportunities and interest and an inability to provide sufficient language environment and context. Virtual reality technology provides new possibilities to solve these problems. Through in-depth research and analysis, this article designs and develops a Chinese language teaching system based on virtual reality technology. This system utilizes virtual reality technology to create a simulated 3D Chinese language environment, enabling learners to experience interactive experiences firsthand. The system includes speech recognition, natural language processing, and artificial intelligence, enabling real-time language communication and student interaction. This system s design considers students learning needs and interests, allowing them to practice their Chinese application skills in real-world simulated environments. This system has broad prospects in Chinese language education and provides new research and innovation directions for educational institutions and developers.

Four year evaluation of a parent advisory group to support a research program for knowledge translation in child health

BackgroundIn 2016, we developed a pediatric parent advisory group to inform our research program which creates innovative knowledge translation (KT) tools for parents on priority topics related to acute childhood illness. We implemented a mixed methods strategy to evaluate the experiences of group members. The purpose of this paper is to present the findings from parent evaluations over four years and to discuss our experiences collaborating with the group over a multi-year period.MethodsWe conducted year-end surveys and interviews of group members to understand parents’ perceptions of their experiences, group management, researcher interaction, and other outcomes of advisory group participation from 2018 to 2021. We applied a mixed methods approach, collecting and analyzing both quantitative (survey) and qualitative (survey/interview) data. Survey data were analyzed by term using descriptive statistics (i.e., frequencies, percentages). Open-ended survey responses were analyzed by conventional content analysis. Interview data were analysed thematically.ResultsYear-end survey response rates and interview participation varied over the years. Responses to evaluation questions were generally positive and most improved over time. Results prompted changes to improve P-PAG operations, such as changes to location of meetings, communications about the group’s purpose, offering sufficient context for discussion items, and providing feedback about how members’ input was used. Themes identified from the qualitative data related to the importance of certain aspects of group functioning, positive views of the group’s current management, and potential areas for improvement. Parents regularly expressed a desire for more diversity in the group’s membership and an interest in hearing more about how the research program’s activities fit into the broader healthcare system and their impacts on health outcomes.ConclusionsOur experience in establishing, managing, and evaluating a parent advisory group over many years has resulted in valuable insights regarding patient engagement in health research and sustaining an advisory group over time. We have learned that an intentional and iterative approach with regular evaluations and responsive changes has been essential for fostering meaningful engagement. Significant resources are required to maintain the group; in turn, the group has made substantial and diverse contributions to the research program and its outputs.

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease.

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018-2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research.

Hierarchical graph fusion network and a new argumentative dataset for multiparty dialogue discourse parsing

Discourse parsing in multi-party dialogue aims to extract the relationships between elementary discourse units (EDUs) such as arguments and utterances, and has numerous applications like chatbots or virtual assistants. Two significant challenges have been encountered in previous works: the obstacles in fusing various contexts in the modeling; and the lack of argumentative dialogue datasets in this field. To tackle context fusion challenges in the modeling, we introduce the Hierarchical Graph Fusion Network (HGFN). This method introduces sufficient contexts by hierarchically modeling the dialogue and minimizes context noise through a novel routing mechanism. It specifically: (1) Encodes multiple levels of contexts using hierarchical graph neural networks. During this stage, the router allows information exchange across different levels, expanding the model’s receptive field in dialogue. (2) Fuses matching signals from multiple levels of contexts with a fusion network. Here, the router restricts the information flows to the same context level, efficiently minimizing noise from irrelevant context. Furthermore, despite the significance of argumentative multi-party dialogue in real-world applications, this area remains largely unexplored due to dataset scarcity. To address this challenge, we develop two meticulously annotated datasets, MRDL and MRDR. Unlike the prevailing datasets that primarily focus on short and colloquial conversations, our datasets feature intricate argumentative dialogues and are publicly accessible at https://github.com/AI0Research/MRDL-and-MRDR. Our new datasets and the HGFN model could promote further advancements in this field. Extensive experiments are conducted and it is revealed that the HGFN model surpasses the state-of-the-art, particularly in complex, argumentative dialogues.

“You are Telling the Story Yourself”: Defining and Developing Narrative Pictorial Warning Labels

ABSTRACT Pictorial warning labels (PWLs) featuring narrative content are promising strategies for communicating health risks and motivating behavior change. The objectives of this study were to (1) identify what intrinsic features a PWL must have to be constructed as a narrative and (2) uncover in what ways narrative PWLs are perceived as being (in)effective. Seven online focus groups were conducted via Zoom with moderate and heavy drinkers (n = 30). Participants discussed a series of mockup PWLs designed to communicate the cancer risk of alcohol. The discussion revealed that a static image must include character, causality, and setting to help individuals construct the story. Specifically, the character should be discernible and believable so that individuals can infer risk information. Moreover, the connection between the image and text should imply a causal relationship between alcohol drinking and cancer risks. Lastly, there should be sufficient relevant background or context information. When discussing the label effectiveness, most participants thought narrative PWLs were more effective than graphic, non-narrative PWLs at informing consumers about the cancer risk of alcohol. Their reasoning included narrative PWLs (1) being easy to understand, (2) evoking curiosity and imagination, (3) eliciting sympathy for the character, (4) not causing aversion, and (5) increasing risk perceptions. This study contributes to the narrative persuasion research and offers practical implications for designing image-based narratives.

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory.

Orbital-free density functional theory (OF-DFT) holds promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of electron density alone. Here, we set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn-Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths, and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two-electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy.

GFRNet: Rethinking the global contexts extraction in medical images segmentation through matrix factorization and self‐attention

AbstractDue to the large fluctuations of the boundaries and internal variations of the lesion regions in medical image segmentation, current methods may have difficulty capturing sufficient global contexts effectively to deal with these inherent challenges, which may lead to a problem of segmented discrete masks undermining the performance of segmentation. Although self‐attention can be implemented to capture long‐distance dependencies between pixels, it has the disadvantage of computational complexity and the global contexts extracted by self‐attention are still insufficient. To this end, the authors propose the GFRNet, which resorts to the idea of low‐rank matrix factorization by forming global contexts locally to obtain global contexts that are totally different from contexts extracted by self‐attention. The authors effectively integrate the different global contexts extract by self‐attention and low‐rank matrix factorization to extract versatile global contexts. Also, to recover the spatial contexts lost during the matrix factorization process and enhance boundary contexts, the authors propose the Modified Matrix Decomposition module which employ depth‐wise separable convolution and spatial augmentation in the low‐rank matrix factorization process. Comprehensive experiments are performed on four benchmark datasets showing that GFRNet performs better than the relevant CNN and transformer‐based recipes.

Modeling reduced speech using a simulated vocal tract and overlapping acoustic events

Reduced utterances are characterized by the alteration or deletion of segments (i.e., syllables, words) and often occur in spontaneous or casual speech styles. These reductions are common in everyday communication and are generally understood given sufficient context. However, modeling reduced speech is not trivial, as the effects of context, articulatory limitations, idiosyncrasies, and speech rate need to be considered in order to approximate acoustic observations of reduction. We utilize a speech production model which modulates a simulated vocal tract according to acoustic events organized along a time axis. The present study focuses on validating its capacity for modeling reduced speech. Evidence of its effectiveness is provided by way of synthetic example utterances, each accompanied by a description of the steps taken to synthesize the utterance and acoustic comparison to an unreduced equivalent. Word-medial consonant reductions will be studied first before proceeding to massive reductions where entire syllables and words are affected. We iteratively synthesize reduced utterances until they are evaluated through informal listening to be comparable to real acoustic observations. The results of this study are discussed as validation of the idea that acoustic events can be arranged to model reductions.