693 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Simply Realising an Imprecise Polyline is NP-hard
We consider the problem of deciding, given a sequence of regions, if there is a choice of points, one for each region, such that the induced polyline is simple or weakly simple, meaning that it can touch but not cross itself. Specifically, we consider the case where each region is a translate of the same shape. We show that the problem is NP-hard when the shape is a unit-disk or unit-square. We argue that the problem is is NP-complete when the shape is a vertical unit-segment
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Occlusion-Ordered Semantic Instance Segmentation
Conventional semantic ‘instance’ segmentation methods offer a segmentation mask for each object instance in an image along with its semantic class label. These methods excel in distinguishing instances, whether they belong to the same class or different classes, providing valuable information about the scene. However, these methods lack the ability to provide depth-related information, thus unable to capture the 3D geometry of the scene.
One option to derive 3D information about a scene is monocular depth estimation. It predicts the absolute distance from the camera to each pixel in an image. However, monocular depth estimation has limitations. It lacks semantic information about object classes. Furthermore, it is not precise enough to reliably detect instances or establish depth order for known instances.
Even a coarse 3D geometry, such as the relative depth or occlusion order of objects is useful to obtain rich 3D-informed scene analysis. Based on this, we address occlusion-ordered semantic instance segmentation (OOSIS), which augments standard semantic instance segmentation by incorporating a coarse 3D geometry of the scene. By leveraging occlusion as a strong depth cue, OOSIS estimates a partial relative depth ordering of instances based on their occlusion relations. OOSIS produces two outputs: instance masks and their classes, as well as the occlusion ordering of those predicted instances.
Existing works pre-date deep learning and rely on simple visual cues such as the y-coordinate of objects for occlusion ordering. This thesis introduces two deep learning-based approaches for OOSIS. The first approach, following a top-down strategy, determines pairwise occlusion order between instances obtained by a standard instance segmentation method. However, this approach lacks global occlusion ordering consistency, having undesired cyclic orderings. Our second approach is bottom-up. It simultaneously derives instances and their occlusion order by grouping pixels into instances and assigning occlusion order labels. This approach ensures a globally consistent occlusion ordering. As part of this approach, we develop a novel deep model that predicts the boundaries where occlusion occurs plus the orientation of occlusion at the boundary, indicating which side of it occludes the other. The output of this model is utilized to obtain instances and their corresponding ordering by our proposed discrete optimization formulation.
To assess the performance of OOSIS methods, we introduce a novel evaluation metric capable of simultaneously evaluating instance segmentation and occlusion ordering. In addition, we utilize standard metrics for evaluating the quality of instance masks. We also evaluate occlusion ordering consistency, and oriented occlusion boundaries. We conduct evaluations on KINS and COCOA datasets
Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Constrained Planarity in Practice -- Engineering the Synchronized Planarity Algorithm
In the constrained planarity setting, we ask whether a graph admits a planar
drawing that additionally satisfies a given set of constraints. These
constraints are often derived from very natural problems; prominent examples
are Level Planarity, where vertices have to lie on given horizontal lines
indicating a hierarchy, and Clustered Planarity, where we additionally draw the
boundaries of clusters which recursively group the vertices in a crossing-free
manner. Despite receiving significant amount of attention and substantial
theoretical progress on these problems, only very few of the found solutions
have been put into practice and evaluated experimentally.
In this paper, we describe our implementation of the recent quadratic-time
algorithm by Bl\"asius et al. [TALG Vol 19, No 4] for solving the problem
Synchronized Planarity, which can be seen as a common generalization of several
constrained planarity problems, including the aforementioned ones. Our
experimental evaluation on an existing benchmark set shows that even our
baseline implementation outperforms all competitors by at least an order of
magnitude. We systematically investigate the degrees of freedom in the
implementation of the Synchronized Planarity algorithm for larger instances and
propose several modifications that further improve the performance. Altogether,
this allows us to solve instances with up to 100 vertices in milliseconds and
instances with up to 100 000 vertices within a few minutes.Comment: to appear in Proceedings of ALENEX 202
Geometric Embeddability of Complexes Is ??-Complete
We show that the decision problem of determining whether a given (abstract simplicial) k-complex has a geometric embedding in ?^d is complete for the Existential Theory of the Reals for all d ? 3 and k ? {d-1,d}. Consequently, the problem is polynomial time equivalent to determining whether a polynomial equation system has a real solution and other important problems from various fields related to packing, Nash equilibria, minimum convex covers, the Art Gallery Problem, continuous constraint satisfaction problems, and training neural networks. Moreover, this implies NP-hardness and constitutes the first hardness result for the algorithmic problem of geometric embedding (abstract simplicial) complexes. This complements recent breakthroughs for the computational complexity of piece-wise linear embeddability
Investigating the use of multimodal screencasts to teach disciplinary concepts in higher education.
This research study explores the use of multimodal lecture screencasts to teach disciplinary concepts in an Irish higher education (HE) context. It builds on an Inquiry Graphics (IG) framework, extending it into a multimodal inquiry framework (MMI) to examine screencasts crafted by lecturers to teach key concepts within their discipline. Multimodality is a widely recognised and applied approach that observes communication as including language but also encompassing other modes of communication, such as sound, image, touch, gesture, feeling, etc. However, studies that provide an in-depth examination of multimodality in teaching and learning in higher education are still scarce. The proposed MMI framework provides a lens to explore graphic-pictorial, linguistic, aural, and spatial- design modes and analyse the semiotic organisation of lecturers’ screencasts, to understand how multimodality relates to teaching and reveals lecturers’ semiotic choices. Qualitative IG elicitation interviews were conducted with 16 HE lecturers from a range of disciplines, where the IG framework provided an analytical opportunity to co-examine the underlying assumptions about how content is presented multimodally. An awareness of the semiotic dimensions of each mode was uncovered, along with structures within the lecturers’ sociocultural context which influenced their decision-making. The use of the MMI framework revealed the semiotic purpose of the graphic-pictorial elements primarily as unprobed representations of the chosen concept. Linguistic choices helped explain the concept within the discipline, while prosodic features of the voice, along with music, were often used intentionally by the lecturer to highlight the relative importance of the elements on screen. The enactment of software features in the screencast design indicated lecturers’ embodied cognition through multimedia, along with digital fluency. The MMI framework may be a helpful teaching tool to support HE lecturers in video and multimedia analysis to unpack the plurality of conceptual representations within multimodal digital artefacts
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