Recent Attempts Research Articles

The Sublime Reimagined: Moving Towards Deweeper Human–Nature Relations in Outdoor Adventure Education

Background: Current research in outdoor adventure education advocates for deeper attention to place and the role that place and the more-than-human world play in pedagogical processes. However, historical and socio-cultural analysis of the roots of OAE reveals an educational approach that encourages adversarial human–nature relations toward the pursuit of anthropocentric outcomes. The use of sublime environments towards these ends does not naturally align with the more recent attempts to foster environmental behaviors through OAE. Purpose: This research addresses the ambiguity between “place” and “pedagogy” to suggest a reimagined role of the sublime that fosters more empathetic human–nature relations. Methodology/Approach: This research utilizes philosophical phenomenology and postqualitative analysis to develop an argument for engaging with sublime environments in ways that cultivate empathetic human–nature relations. Findings/Conclusions: I argue for an awareness of the material aspect of sublime experiences that precedes construction and can inspire empathetic and reciprocal human–nature relations. Implications: Reimagining the sublime as a material phenomenon that is relational and promotes virtues of humility, respect, and care for the more-than-human world.

Strive for Measurement, Set New Standards, and Try Not to Be Evil

I consider recent attempts to establish standards, principles, and goals for artificial intelligence (AI) through the lens of educational measurement. Distinctions are made between generative AI and AI-adjacent methods and applications of AI in formative versus summative assessment contexts. While expressing optimism about its possibilities, I caution that the examples of truly generative AI in educational testing have the potential to be overexaggerated, that efforts to establish standards for AI should complement the Standards for Educational and Psychological Testing and focus attention on the issues of fairness and social responsibility, and that scientific advance and transparency in the development and application of AI in educational assessment may be incompatible with the competitive marketplace that is funding this development.

Rewritable printing of ionic liquid nanofilm utilizing focused ion beam induced film wetting

Manipulating liquid flow over open solid substrate at nanoscale is important for printing, sensing, and energy devices. The predominant methods of liquid maneuvering usually involve complicated surface fabrications, while recent attempts employing external stimuli face difficulties in attaining nanoscale flow control. Here we report a largely unexplored ion beam induced film wetting (IBFW) technology for open surface nanofluidics. Local electrostatic forces, which are generated by the unique charging effect of Helium focused ion beam (HFIB), induce precursor film of ionic liquid and the disjoining pressure propels and stabilizes the nanofilm with desired patterns. The IBFW technique eliminates the complicated surface fabrication procedures to achieve nanoscale flow in a controllable and rewritable manner. By combining with electrochemical deposition, various solid materials with desired patterns can be produced.

Legal actors' and laypersons' utility judgments of eyewitness lineup procedures and outcomes.

Recent attempts to model the relative performances of eyewitness lineup procedures necessarily include theoretical assumptions about the various costs/benefits, or utilities, of different identification outcomes. We collected data to incorporate empirically derived utilities into such modeling as well as data on various stakeholders' views of lineup procedures as tertiary objectives. This research was exploratory; therefore, we did not have a priori hypotheses. We surveyed judges' (n = 70), prosecutors' (n = 28), police officers' (n = 82), and laypersons' (n = 191) opinions about eyewitness identification procedures and the utilities of outcomes of eyewitness identification procedures. We incorporated the utility judgments into models comparing the desirability of various lineup reforms and compared policy preferences between our samples. All samples frequently mentioned estimator and system variables in open-ended evaluations of lineup procedures, but legal samples mentioned system variables more often than did laypersons. Reflector variables (e.g., confidence) were mentioned less often across the board, as was the scientific basis/standardization of identification policy (especially among laypersons). Utility judgments of various identification outcomes indicated that judges adopt values more closely aligned with normative legal ethics (i.e., the Blackstone ratio), whereas other stakeholders (especially laypersons) depart significantly from those standards. Utility models indicated general agreement among samples in lineup procedure preferences, which varied as a function of culprit-presence base rates. Although legal stakeholders vary in how they value eyewitness identification outcomes, their values imply relatively consistent policy preferences that sometimes depart from scientific recommendations. Nonetheless, all samples expressed support for using scientific research to inform legal policy regarding eyewitness evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Human Agency and the Technoscientific Dilemma: Contesting the Role of Technology in Shaping our Collective Futures

The launch of ChatGPT created a tidal wave of interest, rapid uptake of the app and a surge of capital investment. It quickly became clear that what was now being called ‘generative AI’ would have multiple impacts and implications across the board. The paper begins by outlining aspects of the context from which these innovations arose. They include Neoliberalism and the ‘playbook’ of careless, ‘non-legal’ innovation that occurred during the early rise of social media platforms. The paper also critiques Silicon Valley, both for its one-sided culture and its obsession with marketing. It specifically draws attention to the characteristics and limitations of ‘technoscientific’ outlooks and the way they conceal, or edit out, key aspects of human existence. A summary of the attributes of technoscience suggests a number of possible responses. Behind these, however, is a widely ignored dilemma - the proposition that a continuation of further stages of high-tech innovation benefits the human enterprise. Yet many high-tech developments are unambiguously sociopathic. They project extreme danger and dysfunction out onto innocent people and unprepared social environments. The paper summarises recent attempts to come to terms with a radically transformed, technology saturated environment and concludes with suggestions for enhancing human agency.

Modeling Environmental Impacts of Intensive Shrimp Aquaculture: A Three-Dimensional Hydrodynamic Ecosystem Approach

Already a multibillion-dollar global industry, shrimp aquaculture, is growing all the time. The intensive method, which is the most common method in shrimp aquaculture, remains commercially challenged due to the expenditures associated with environmental pollution abatement. Although the comprehensive understanding of this intricate aquaculture environment has been advanced using mathematical modeling, recent attempts to improve the model’s structure have not yielded enough results. This work upgraded the previous method to a three-dimensional hydrodynamic ecosystem model with the effects of shrimps being replaced by approximation equations for the environmental assessment of a shrimp aquaculture pond in Kyushu District, Japan. Our approach was successful, as demonstrated by the high consistency of the simulation results when compared to observation data and the previous results. Additionally, we first revealed the impacts of stratification and confirmed the notable daily variation in the water quality. Our case study offers significant practical information on the characteristics of intensive shrimp aquaculture, implications for long-term sustainable operations, and future research priorities on local-scale ecosystem modeling.

MFOS: Model-Free & One-Shot Object Pose Estimation

Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scalability. Notably, recent attempts have been made to solve this issue, but they still require accurate 3D data of the object surface at both train and test time. In this paper, we introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. In contrast to existing state-of-the-art approaches, which rely on task-specific modules, our proposed model is entirely based on a transformer architecture, which can benefit from recently proposed 3D-geometry general pretraining. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark. Finally, extensive ablations allow us to determine good practices with this relatively new type of architecture in the field.

Towards Human-like Learning from Relational Structured Data

Relational structured data is a way of representing knowledge using nodes and edges, while also capturing the meaning of that knowledge in a structured form that can be used for machine learning. Compared with vision and natural language data, relational structured data represents and manipulates structured knowledge, which can be beneficial for tasks that involve reasoning or inference. On the other hand, vision and NLP deal more with unstructured data (like images and text), and they often require different types of models and algorithms to extract useful information or features from the data. Human-like Learning develops methods that can harness relational structures and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. With Human-like Learning, the learning algorithm is efficient and can adapt to new or unseen situations, which is crucial in real-world applications where environments may change unpredictably. Moreover, the models are easier for humans to understand and interpret, which is important for transparency and trust in AI systems. In this talk, we present our recent attempts towards human-like learning from relational structured data.

Spot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator

Layout generation is a critical step in graphic design to achieve meaningful compositions of elements. Most previous works view it as a sequence generation problem by concatenating element attribute tokens (i.e., category, size, position). So far the autoregressive approach (AR) has achieved promising results, but is still limited in global context modeling and suffers from error propagation since it can only attend to the previously generated tokens. Recent non-autoregressive attempts (NAR) have shown competitive results, which provides a wider context range and the flexibility to refine with iterative decoding. However, current works only use simple heuristics to recognize erroneous tokens for refinement which is inaccurate. This paper first conducts an in-depth analysis to better understand the difference between the AR and NAR framework. Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e.g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input. We show that it serves as a complementary modality to the element sequence in object space and contributes greatly to the overall performance. Experiments on two public datasets show that our approach outperforms both AR and NAR baselines. Extensive studies further prove the effectiveness of different modules with interesting findings. Our code will be available at https://github.com/ffffatgoose/SpotError.

ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis

The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos. However, existing manual methods require laborious annotation, and recent attempts leveraging video perception models are limited to low-level annotations like ball trajectories, overlooking tactics that necessitate an understanding of stroke techniques. State-of-the-art action segmentation models also struggle with technique recognition due to frequent occlusions and motion-induced blurring in racket sports videos. To address these challenges, We propose ViSTec, a Video-based Sports Technique recognition model inspired by human cognition that synergizes sparse visual data with rich contextual insights. Our approach integrates a graph to explicitly model strategic knowledge in stroke sequences and enhance technique recognition with contextual inductive bias. A two-stage action perception model is jointly trained to align with the contextual knowledge in the graph. Experiments demonstrate that our method outperforms existing models by a significant margin. Case studies with experts from the Chinese national table tennis team validate our model's capacity to automate analysis for technical actions and tactical strategies. More details are available at: https://ViSTec2024.github.io/.

Arbitrary-Scale Video Super-resolution Guided by Dynamic Context

We propose a Dynamic Context-Guided Upsampling (DCGU) module for video super-resolution (VSR) that leverages temporal context guidance to achieve efficient and effective arbitrary-scale VSR. While most VSR research focuses on backbone design, the importance of the upsampling part is often overlooked. Existing methods rely on pixelshuffle-based upsampling, which has limited capabilities in handling arbitrary upsampling scales. Recent attempts to replace pixelshuffle-based modules with implicit neural function-based and filter-based approaches suffer from slow inference speeds and limited representation capacity, respectively. To overcome these limitations, our DCGU module predicts non-local sampling locations and content-dependent filter weights, enabling efficient and effective arbitrary-scale VSR. Our proposed multi-granularity location search module efficiently identifies non-local sampling locations across the entire low-resolution grid, and the temporal bilateral filter modulation module integrates content information with the filter weight to enhance textual details. Extensive experiments demonstrate the superiority of our method in terms of performance and speed on arbitrary-scale VSR.

Hydrogen in metallic alloys ─ embrittlement and enhanced plasticity: a review

Abstract The evolution of ideas concerning the nature of hydrogen embrittlement of engineering metallic materials is described based on a number of the proposed hypotheses and corresponding experiments. The main attention is paid to two of them, namely hydrogen-enhanced decohesion (HEDE) and hydrogen-enhanced localized plasticity (HELP). Recent attempts to interconnect the both models as HELP + HEDE and HELP-mediated HEDE ones are also estimated. A conclusion is made that HELP model is preferential for understanding the entire array of experimental data with a caveat that it is necessary to consider the chemical nature of hydrogen atoms and view them not only as point defects. Based on the studies of hydrogen effect on the atomic interactions in iron, nickel, titanium, and its alloys, it is shown that the electron approach to HELP phenomenon adequately describes two competitive features of hydrogen behavior in metals: increased brittleness and enhanced plasticity. Due to the increase in the concentration of free electrons, hydrogen decreases the elasticity moduli, which causes the crystal lattice to soften. For this reason, the formation of hydrogen atmospheres around the dislocations decreases the start stress of dislocation sources, as well as line tension of emitted dislocations, enhancing thereby their mobility, and weakens repulsion between dislocations in their pile-ups. The range of temperatures and strain rates in which hydrogen embrittlement occurs is controlled by the enthalpies of hydrogen atoms’ diffusion and their binding to dislocations. The resulting consequences for mechanical properties depend on the short-range atomic order, SRO, which inherently occurs in the metallic solid solutions and localizes plastic deformation both in the cases of short-range atomic ordering and of short-range atomic decomposition. Hydrogen enhances slip localization because of its different solubility in the submicrovolumes of short-range decomposed solid solutions. If SRO is absent or not remarkably formed, the hydrogen-increased concentration of free electrons results in enhanced plasticity. Available positive hydrogen effects on the plasticity of titanium β-alloys and austenitic steels are presented and interpreted.

„Przeczucie. Gawęda o szarej godzinie” Józefa Bohdana Dziekońskiego jako literacka realizacja kategorii fantastyczności i fantazmatyczności

The article aims to analyze the fantastic and phantasmatic nature of the novella Premonition. The Tale at the Grey Hour by Józef Bohdan Dziekoński (1816–1855) within the latest definitional contexts of these terms, which are often overlooked or completely unused in the research on this writer. In the first part, the author reviews recent attempts to define the essence of 'fantasy' in Dziekoński’s works as per well-known researchers such as Antoni Gromadzki and Michał Szargot, while also acknowledging the contributions of new or yet unrecognized scholars, and then supplements these conclusions. Furthermore, the author incorporates the concept of 'phantasm' as explored in Maria Janion’s study On Phantasmatic Criticism, which has not yet been applied in research on Dziekoński’s works. The second part is devoted to the analysis and interpretation of the fantastic and phantasmatic character of Dziekoński’s little-known novella Premonition. The Tale at the Grey Hour. In applying the theoretical issues from the first part to the reading of this story, the author highlights the novella’s significance, where the ideals and worldview of the writer are embodied. This underscores the researcher's belief in Dziekoński’s ideological consistency evident in his literary works. Dziekoński’s reflections on fantasy as a literary genre, or a broadly understood method of literary expression, are also discussed. According to him, fantasy literature must fulfill specific socio-cultural criteria to properly align with its era and gain acceptance. The novella Premonition. The Tale at the Grey Hour exemplifies the author’s implementation of these ideas and worldviews, a point strongly emphasized in the article.

Suicide prevention for substance using youth experiencing homelessness: study protocol for a randomized controlled trial

BackgroundWhile research on substance using youth experiencing homelessness (YEH) is increasing, there is a dearth of information regarding effective prevention interventions for these youth. Suicide is the leading cause of death among YEH and most youth do not access services that may be available to them. Therefore, this study seeks to address this gap in the research literature with the goal to identify an effective suicide prevention intervention that can be readily adopted by communities that serve these youth.MethodsThree hundred (N = 300) YEH with recent substance use and suicidal ideation or a recent suicide attempt will be recruited from the streets as well as a drop-in center serving YEH. After the baseline assessment, all youth will be randomly assigned to Cognitive Therapy for Suicide Prevention (CTSP) + Services as Usual (SAU) (N = 150) or to SAU alone (N = 150). SAU includes outreach, advocacy, and service linkage whereas YEH who receive CTSP will also receive ten CTSP sessions and an optional nine booster sessions. Follow-up assessments will be conducted at 3, 6, 9, and 12 months post-baseline. Theoretically derived mediators (e.g., cognitive distortions) will be tested to shed light on mechanisms associated with change, and the moderating effects of sex, race, sexual orientation, and baseline service connection will be examined. In order to ease future dissemination of the intervention to agencies serving YEH, we will rigorously assess acceptability, feasibility, fidelity, and cost associated with the delivery of our intervention approach using a mixed-methods approach.DiscussionThis study adds to a very small number of clinical trials seeking to prevent lethal suicide among a very high-risk group by addressing suicidal ideation directly rather than underlying conditions. It is hypothesized that youth receiving CTSP + SAU will show greater reductions in suicidal ideation (primary outcome), substance use, and depressive symptoms (secondary outcomes) over time compared to SAU alone, as well as improved risk and protective factors.Trial registrationNCT05994612. Date of Registration: August 16, 2023.

Evaluation of molecular subtypes of breast cancer using MRI BI-RADS Lexicon

BackgroundMolecular subtyping of breast cancer is one of the prognostic factors which play a very important role in managing patient’s treatment plan. The MRI BI-RADS Lexicon is initially used to categorize breast lesions but recent attempts were employed to differentiate breast lesions based on their molecular subtypes using this lexicon. The study aimed to evaluate of the role of the MRI BI-RADS Lexicon in classifying different molecular subtypes of breast cancer especially after coupling with Kaiser scoring system.MethodsThis retrospective study was conducted on 147 patients with 170 malignant breast lesions. They underwent Pre-contrast and a Dynamic contrast MRI study. Retrospective interpretation of the morphological and dynamic criteria of the breast lesions based on the MRI BI-RADS criteria was carried out followed by reassessment of the same lesions by Kaiser scoring. Resulting data were correlated with histopathological and immunological characterization.ResultsLuminal subtypes were more frequently encountered as mass lesions, contrary to the Non-Luminal lesions which showed a more frequent non-mass presentation value (P 0.002). The shape, margin, internal enhancement pattern of the mass lesions showed significant variability between different molecular subtypes (P < 0.001, < 0.001, < 0.001) respectively. On Dynamic study, Plateau curve was a more evident pattern with Luminal lesions contrasting with their Non-Luminal counterparts which showed a washout pattern more frequently (P 0.0004). Most of luminal A cancers were presented as mass lesions with rim enhancement and categorized as BIRADS 4 while most of Her2neu positive cancers (including luminal B entity) were presented as non-mass lesions with irregular shape and dark internal septations and categorized as BIRADS 5 with statistically significant values (P < 0.001). Coupling with Kaiser scoring system improved the categorization of non-luminal tumors as BIRADS 5 lesions especially the aggressive TN cancers.ConclusionsMRI-BIRADS lexicon can be of great value in the non-invasive molecular characterization of breast cancer. Kaiser score improved the categorization of TN cancers which were upgraded to BIRADS 5 category.

Inferring the dark matter splashback radius from cluster gas and observable profiles in the FLAMINGO simulations

ABSTRACT The splashback radius, coinciding with the minimum in the dark matter radial density gradient, is thought to be a universal definition of the edge of a dark matter halo. Observational methods to detect it have traced the dark matter using weak gravitational lensing or galaxy number counts. Recent attempts have also claimed the detection of a similar feature in Sunyaev–Zel’dovich (SZ) observations of the hot intracluster gas. Here, we use the FLAMINGO simulations to investigate whether an extremum gradient in a similar position to the splashback radius is predicted to occur in the cluster gas profiles. We find that the minimum in the gradient of the stacked 3D gas density and pressure profiles, and the maximum in the gradient of the entropy profile, broadly align with the splashback feature though there are significant differences. While the dark matter splashback radius varies with specific mass accretion rate, in agreement with previous work, the radial position of the deepest minimum in the log-slope of the gas density is more sensitive to halo mass. In addition, we show that a similar minimum is also present in projected 2D pseudo-observable profiles: emission measure (X-ray), Compton-y (SZ), and surface mass density (weak lensing). We find that the latter traces the dark matter results reasonably well albeit the minimum occurs at a slightly smaller radius. While results for the gas profiles are largely insensitive to accretion rate and various observable proxies for dynamical state, they do depend on the strength of the feedback processes.

Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

High-resolution velocity determination from particle images via neural networks with optical flow velocimetry regularization

Particle image velocimetry (PIV) and optical flow velocimetry (OFV) are important velocity measurement methods in the field of fluid dynamics. Nevertheless, the conventional cross correlation-based PIV method is beset by diminished resolution, while the OFV method exhibits computational sluggishness and susceptibility to noise. These constraints have somewhat delimited the applicability of PIV and OFV techniques. Recent attempts have introduced deep learning-based methods for analyzing PIV images, offering high-resolution velocity fields with computational efficiency, but their accuracy needs improvement. This study proposes four neural networks based on the well-established FlowNetS. They incorporate two distinct velocity constraints, namely, first-order velocity smoothing regularization and second-order grad (curl)–grad (div) regularization. In the networks, these constraints are used either independently or in combination with optical flow conservation (OFC). The performances of the networks have been assessed on six different flow configurations, and the results show that the network with the second-order regularization markedly outperforms the original network across all flows, demonstrating an enhanced capacity to capture larger-scale vortices. The network with the first-order regularization also exhibits superior performance compared to the original network except in the case of cylinder flow. Unexpectedly, the introduction of the OFC constraints results in a decline in network performance. This anomaly may stem from the network's inherent capability to capture optical flow features, rendering the OFC constraint less effective in providing guidance. In summary, this study underscores the substantial potential of neural networks incorporated with judicious physical constraints in PIV applications, enabling the determination of high-resolution, high-accuracy flow fields.

APA policy supports gender‐affirming evidence‐based care

The American Psychological Association (APA) last week announced the adoption of a landmark policy‐affirming, evidence‐based statement on health care and clinical care for transgender, gender diverse and nonbinary children, adolescents and adults, noting that recent legislative attempts to obstruct access to psychological and medical interventions for such individuals puts them at risk of depression, anxiety and other negative mental health outcomes.

Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force Model

The traditional social force model (SFM) in crowd simulation experiences difficulty coping with the complexity of the crowd, limited by singular physical formulas and parameters. Recent attempts to combine deep learning with these models focus more on simulating specific states of crowds. This paper introduces an advanced deep social force model, influenced by crowd states. It utilizes deep neural networks to accurately fit crowd trajectory features, enhancing behavior simulation capabilities. Geometrical constraints within the model provide control over varied crowd behaviors, adjustable to simulate different crowd types. Before training, we use the SFM to refine behaviors in real trajectories with excessively small distances, aiming to enhance the general applicability of the model. Comparative experiments affirm the effectiveness of the model, showing comparable performance to both classic physical models and modern learning-based hybrid models in pedestrian simulations, with reduced collisions. In addition, the model has a certain ability to simulate crowds with high density and diverse behaviors.