Neurodivergent students bring diverse cognitive styles and work patterns, and they are often a key audience for digital distraction blockers aimed at managing attention. However, it remains unclear whether these tools are grounded in their lived experiences, raising concerns that tool design may overlook neurodivergent practices and inadvertently reinforce neuronormative perspectives. We conducted semi-structured interviews with 27 post-secondary students with ADHD, Autism Spectrum Disorder, and/or Generalized Anxiety Disorder to examine how they use distraction blockers. Our thematic analysis shows how neurodivergent students adapt blockers for regulating stimulation levels, but also encounter tensions between their work rhythms and tool design rooted in fixed, linear time structures, which may exacerbate self-stigmatizing comparisons. We call for distraction blockers that empower neurodivergent strengths by normalizing and scaffolding diverse ways of working, such as hyperfocus and non-linear workflows, and help navigate known tensions between flexibility and structure towards more inclusive digital well-being tools.

Expressing oneself appropriately in online meetings through non-verbal cues can be challenging for knowledge workers. Automatic non-verbal cue detection technologies have the potential to support workers' self-presentation efforts through real-time feedback, but little is known about workers' reactions to and the implications of doing so. We designed and implemented Novecs as a technology probe of a real-time feedback display that automatically detects and signals users' own non-verbal cues -- smiling, nodding, gaze, and posture. Novecs was deployed in an exploratory field study (n=18) to support knowledge workers' self-presentation in their everyday meetings. Post-study interviews reveal how Novecs' real-time feedback helped increase in-the-moment self-awareness, and how neutrally-framed feedback may help navigate tensions between authentic and in-authentic self-presentation. Participants also emphasized the need for natural timing when adjusting non-verbal cues in-meeting. We discuss design opportunities and challenges of real-time, non-verbal cue feedback systems, such as personalizing feedback based on different meeting types.

Today’s knowledge workers face cognitively demanding tasks and blurred work-life boundaries amidst rising stress and burnout in the workplace. Holistic approaches to supporting workers, which consider both productivity and well-being, are increasingly important. Taking this holistic approach, we designed an intervention inspired by cognitive behavioral therapy that consists of: (1) using the term “Time Well Spent” (TWS) in place of “productivity”, (2) a mobile self-logging tool for logging activities, feelings, and thoughts at work, and (3) a visualization that guides users to reflect on their data. We ran a 4-week exploratory qualitative comparison in the field with 24 graduate students to examine ourTherapy-inspiredintervention alongside a classicBaselineintervention. Participants who used our intervention often shifted toward a holistic perspective of their primary working hours, which included an increased consideration of breaks and emotions. No such change was seen by those who used theBaselineintervention.

This letter proposes methods to improve data size and access time for$k^{2}$-raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with$k^{2}$-raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for$k^{2}$-raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.

The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.

Point cloud data have been put under the spotlight by many applications that play an increasingly important role in our every day lives. Their large size and ever-growing prevalent use cases have raised the interest in specialized compression algorithms for point cloud data. In this paper we propose a lossless intra-frame encoder for point cloud geometry. It relies on a single projection of the entire point cloud on a predetermined plane, combined with a context-adaptive binary arithmetic encoder. Our approach simplifies the current best performing approach for intra-frame compression. The experimental results indicate that our proposal not only improves the performance of all other intra-frame approaches, but it even surpasses the performance of state-of-the-art inter-frame approaches. Furthermore, we suggest to replace the adaptive encoder with a semi-adaptive approach for further performance gains.

Dynamic biological networks model changes in the network topology over time. However, often the topologies of these networks are not available at specific time points. Existing algorithms for studying dynamic networks often ignore this problem and focus only on the time points at which experimental data is available. In this paper, we develop a novel alignment based network construction algorithm, ANCA, that constructs the dynamic networks at the missing time points by exploiting the information from a reference dynamic network. Our experiments on synthetic and real networks demonstrate that ANCA predicts the missing target networks accurately, and scales to large-scale biological networks in practical time. Our analysis of an E. coli protein-protein interaction network shows that ANCA successfully identifies key temporal changes in the biological networks. Our analysis also suggests that by focusing on the topological differences in the network, our method can be used to find important genes and temporal functional changes in the biological networks.

The modern workplace is more demanding than ever before. Yet, since the industrial age, productivity measures have predominantly stayed narrowly focused on the output of the work, and not accounted for the big shift in the cognitive demands placed on the workers or the interleaving of work and life that is so common today. We posit that a more holistic conceptualization of Time Well Spent (TWS) at work could mitigate this issue. In our 1-week study, 40 knowledge workers used the experience sampling method (ESM) to rate their TWS and then define TWS at the end of the week. Our work contributes a preliminary characterization of TWS and empirical evidence that this term can capture a more holistic notion of work that also includes the worker's feelings and well-being.

Studies on collaborative virtual environments (CVEs) have suggested capture and later replay of multimodal interactions (e.g., speech, body language, and scene manipulations), which we refer to as multimodal recordings, as an effective medium for time-distributed collaborators to discuss and review 3D content in an immersive, expressive, and asynchronous way. However, there exist gaps of empirical knowledge in understanding how this multimodal asynchronous VR collaboration (MAVRC) context impacts social behaviors in mediated-communication, workspace awareness in cooperative work, and user requirements for authoring and consuming multimedia recording. This study aims to address these gaps by conceptualizing MAVRC as a type of CSCW and by understanding the challenges and design considerations of MAVRC systems. To this end, we conducted an exploratory need-finding study where participants (N = 15) used an experimental MAVRC system to complete a representative spatial task in an asynchronously collaborative setting, involving both consumption and production of multimodal recordings. Qualitative analysis of interview and observation data from the study revealed unique, core design challenges of MAVRC in: (1) coordinating proxemic behaviors between asynchronous collaborators, (2) providing traceability and change awareness across different versions of 3D scenes, (3) accommodating viewpoint control to maintain workspace awareness, and (4) supporting navigation and editing of multimodal recordings. We discuss design implications, ideate on potential design solutions, and conclude the paper with a set of design recommendations for MAVRC systems.

Robots that effectively manipulate the human body could potentially be useful in a wide variety of applications, including assistive applications for people with disabilities. Toward this end, we present a method to enable robots to compliantly manipulate human limbs. Our approach uses model predictive control (MPC). Given an action by the robot, the model predicts how the human body will move and what forces the robot will apply to the human body. The robot uses this model to optimize its actions to achieve desired motions of the human body while controlling applied forces. This optimization is subject to various constraints, including constraints to avoid hyperextension of the human's joints and to avoid slipping of the robot's end effectors. In this paper, our controller uses a quasistatic model of the human limb in contact with the robot's end effectors, which have linear Cartesian stiffness with respect to Cartesian equilibrium positions. We evaluated our approach in simulation with the specific task of lifting the leg of a human body in a supine position (i.e., lying down). In our tests, we varied the goal configuration for the human leg, the stiffness of the robot's two end effectors, and the model error (i.e., the difference between the controller's model of the human body and the actual human body). Our evaluation demonstrates the feasibility of our approach, since our controller performed well in terms of the forces the robot applied to the human leg and the human leg's motions.